Mathematics Books

Book SynopsisNumber Theory: A Lively Introduction with Proofs, Applications, and Stories, is a new book that provides a rigorous yet accessible introduction to elementary number theory along with relevant applications. Readable discussions motivate new concepts and theorems before their formal definitions and statements are presented. Many theorems are preceded by Numerical Proof Previews, which are numerical examples that will help give students a concrete understanding of both the statements of the theorems and the ideas behind their proofs, before the statement and proof are formalized in more abstract terms. In addition, many applications of number theory are explained in detail throughout the text, including some that have rarely (if ever) appeared in textbooks. A unique feature of the book is that every chapter includes a math myth, a fictional story that introduces an important number theory topic in a friendly, inviting manner. Many of the exerciTable of ContentsPreface. To the Student. To the Instructor. Acknowledgements. 0. Prologue. 1. Numbers, Rational and Irrational. (Historical figures: Pythagoras and Hypatia). 1.1 Numbers and the Greeks. 1.2 Numbers you know. 1.3 A First Look at Proofs. 1.4 Irrationality of he square root of 2. 1.5 Using Quantifiers. 2. Mathematical Induction. (Historical figure: Noether). 2.1.The Principle of Mathematical Induction. 2.2 Strong Induction and the Well Ordering Principle. 2.3 The Fibonacci Sequence and the Golden Ratio. 2.4 The Legend of the Golden Ratio. 3. Divisibility and Primes. (Historical figure: Eratosthenes). 3.1 Basic Properties of Divisibility. 3.2 Prime and Composite Numbers. 3.3 Patterns in the Primes. 3.4 Common Divisors and Common Multiples. 3.5 The Division Theorem. 3.6 Applications of gcd and lcm. 4.The Euclidean Algorithm. (Historical figure: Euclid). 4.1 The Euclidean Algorithm. 4.2 Finding the Greatest Common Divisor. 4.3 A Greeker Argument that the square root of 2 is Irrational. 5. Linear Diophantine Equations. (Historical figure: Diophantus). 5.1 The Equation aX + bY = 1. 5.2 Using the Euclidean Algorithm to Find a Solution. 5.3 The Diophantine Equation aX + bY = n. 5.4 Finding All Solutions to a Linear Diophantine Equation. 6. The Fundamental Theorem of Arithmetic. (Historical figure: Mersenne). 6.1 The Fundamental Theorem. 6.2 Consequences of the Fundamental Theorem. 7. Modular Arithmetic. (Historical figure: Gauss). 7.1 Congruence modulo n. 7.2 Arithmetic with Congruences. 7.3 Check Digit Schemes. 7.4 The Chinese Remainder Theorem. 7.5 The Gregorian Calendar. 7.6 The Mayan Calendar. 8. Modular Number Systems. (Historical figure: Turing). 8.1 The Number System Zn: an Informal View. 8.2 The Number System Zn: Definition and Basic Properties. 8.3 Multiplicative Inverses in Zn. 8.4 Elementary Cryptography. 8.5 Encryption Using Modular Multiplication. 9. Exponents Modulo n. (Historical figure: Fermat). 9.1 Fermat's Little Theorem. 9.2 Reduced Residues and the Euler \phi-function. 9.3 Euler's Theorem. 9.4 Exponentiation Ciphers with a Prime modulus. 9.5 The RSA Encryption Algorithm. 10. Primitive Roots. (Historical figure: Lagrange). 10.1 Zn. 10.2 Solving Polynomial Equations in Zn. 10.3 Primitive Roots. 10.4 Applications of Primitive Roots. 11. Quadratic Residues. (Historical figure: Eisenstein) 11.1 Squares Modulo n 11.2 Euler's Identity and the Quadratic Character of -1 11.3 The Law of Quadratic Reciprocity 11.4 Gauss's Lemma 11.5 Quadratic Residues and Lattice Points. 11.6 The Proof of Quadratic Reciprocity. 12. Primality Testing. (Historical figure: Erdös). 12.1 Primality testing. 12.2 Continued Consideration of Charmichael Numbers. 12.3 The Miller-Rabin Primality test. 12.4 Two Special Polynomial Equations in Zp. 12.5 Proof that Millar-Rabin is Effective. 12.6 Prime Certificates. 12.7 The AKS Deterministic Primality Test. 13. Gaussian Integers. (Historical figure: Euler). 13.1 Definition of Gaussian Integers 13.2 Divisibility and Primes in Z[i]. 13.3 The Division Theorem for the Gaussian Integers. 13.4 Unique Factorization in Z[i]. 13.5 Gaussian Primes. 13.6 Fermat's Two Squares Theorem. 14. Continued Fractions. (Historical figure: Ramanujan). 14.1 Expressing Rational Numbers as Continued Fractions. 14.2 Expressing Irrational Numbers as Continued Fractions. 14.3 Approximating Irrational Numbers Using Continued Fractions. 14.4 Proving that Convergents are Fantastic Approximations. 15. Some Nonlinear Diophantine Equations. (Historical figure: Germain). 15.1 Pell's Equation 15.2 Fermat's Last Theorem 15.3 Proof of Fermat's Last Theorem for n = 4. 15.4 Germain's Contributions to Fermat's Last Theorem 15.5 A Geometric look at the Equation x4 + y4 = z2. Appendix: Axioms of Number Theory. A.1 What is a Number System? A.2 Order Properties of the Integers. A.3 Building Results From Our Axioms. A.4 The Principle of Mathematical Induction.

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Book SynopsisAn accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts. Seven easy-to-follow chapters provide intuitive explanations and in-depth coveTable of ContentsPreface ix 1. Introduction to Forecasting 1 1.1 The Nature and Uses of Forecasts, 1 1.2 Some Examples of Time Series, 5 1.3 The Forecasting Process, 12 1.4 Resources for Forecasting, 14 2. Statistics Background for Forecasting 18 2.1 Introduction, 18 2.2 Graphical Displays, 19 2.3 Numerical Description of Time Series Data, 25 2.4 Use of Data Transformations and Adjustments, 34 2.5 General Approach to Time Series Modeling and Forecasting, 46 2.6 Evaluating and Monitoring Forecasting Model Performance, 49 3. Regression Analysis and Forecasting 73 3.1 Introduction, 73 3.2 Least Squares Estimation in Linear Regression Models, 75 3.3 Statistical Inference in Linear Regression, 84 3.4 Prediction of New Observations, 96 3.5 Model Adequacy Checking, 98 3.6 Variable Selection Methods in Regression, 106 3.7 Generalized and Weighted Least Squares, 111 3.8 Regression Models for General Time Series Data, 133 4. Exponential Smoothing Methods 171 4.1 Introduction, 171 4.2 First-Order Exponential Smoothing, 176 4.3 Modeling Time Series Data, 180 4.4 Second-Order Exponential Smoothing, 183 4.5 Higher-Order Exponential Smoothing, 193 4.6 Forecasting, 193 4.7 Exponential Smoothing for Seasonal Data, 210 4.8 Exponential Smoothers and ARIMA Models, 217 5. Autoregressive Integrated Moving Average (ARIMA) Models 231 5.1 Introduction, 231 5.2 Linear Models for Stationary Time Series, 231 5.3 Finite Order Moving Average (MA) Processes, 235 5.4 Finite Order Autoregressive Processes, 239 5.5 Mixed Autoregressive–Moving Average (ARMA) Processes, 253 5.6 Nonstationary Processes, 256 5.7 Time Series Model Building, 265 5.8 Forecasting ARIMA Processes, 275 5.9 Seasonal Processes, 282 5.10 Final Comments, 286 6. Transfer Functions and Intervention Models 299 6.1 Introduction, 299 6.2 Transfer Function Models, 300 6.3 Transfer Function–Noise Models, 307 6.4 Cross Correlation Function, 307 6.5 Model Specification, 309 6.6 Forecasting with Transfer Function–Noise Models, 322 6.7 Intervention Analysis, 330 7. Survey of Other Forecasting Methods 343 7.1 Multivariate Time Series Models and Forecasting, 343 7.2 State Space Models, 350 7.3 ARCH and GARCH Models, 355 7.4 Direct Forecasting of Percentiles, 359 7.5 Combining Forecasts to Improve Prediction Performance, 365 7.6 Aggregation and Disaggregation of Forecasts, 369 7.7 Neural Networks and Forecasting, 372 7.8 Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures, 375 Appendix A. Statistical Tables 387 Appendix B. Data Sets for Exercises 407 Bibliography 437 Index 443

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Book SynopsisA new edition of the classic guide to the use of statistics in medicine, featuring examples from articles in the New England Journal of Medicine Medical Uses of Statistics has served as one of the most influential works on the subject for physicians, physicians-in-training, and a myriad of healthcare experts who need a clear idea of the proper application of statistical techniques in clinical studies as well as the implications of their interpretation for clinical practice. This Third Edition maintains the focus on the critical ideas, rather than the mechanics, to give practitioners and students the resources they need to understand the statistical methods they encounter in modern medical literature. Bringing together contributions from more than two dozen distinguished statisticians and medical doctors, this volume stresses the underlying concepts in areas such as randomized trials, survival analysis, genetics, linear regression, meta-analyTrade Review"Medical Uses of Statistics, 3rd Edition" presents the concepts of medical statistics across a broad range of topics with a practical perspective, a moderate level of detail, and a minimal number of formulae... The text is clearly written in a consistent style." (Journal of Clinical Research Best Practices, November 2010) Table of ContentsContributors. Preface. Preface to the Second Edition. Preface to the First Edition. Acknowledgments. Origins of Chapters. Introduction. SECTION I: BROAD CONCEPTS AND ANALYTIC TECHNIQUES. Chapter 1 Statistical Concepts Fundamental to Investigations (Lincoln E. Moses). Chapter 2 Some Uses of Statistical Thinking (John C. Bailar III). Chapter 3 Use of Statistical Analysis in the New England Journal of Medicine (Shilpi Agarwal, Graham A. Colditz, and John D. Emerson). SECTION II: DESIGN. Chapter 4 Randomized Trials and Other Parallel Comparisons of Treatment (Nancy E. Mayo). Chapter 5 Crossover and Self-Controlled Designs in Clinical Research (John C. Bailar III, Thomas A. Louis, Philip W. Lavori, and Marcia Polansky). Chapter 6 The Series of Consecutive Cases as a Device for Assessing Outcomes of Interventions (Lincoln E. Moses). Chapter 7 Biostatistics in Epidemiology: Design and Basic Analysis (Mark S. Goldberg). SECTION III: ANALYSIS. Chapter 8 p-Values (James H. Ware, Frederick Mosteller, Fernando Delgado, Christl Donnelly, and Joseph A. Ingelfinger). Chapter 9 Understanding Analyses of Randomized Trials (Nancy E. Mayo). Chapter 10 Linear Regression in Medical Research (Paul J. Rathouz and Amita Rastogi). Chapter 11 Statistical Analysis of Survival Data (Stephen W. Lagakos). Chapter 12 Analysis of Categorical Data in Medical Studies (Paul S. Albert). Chapter 13 Analyzing Data from Ordered Categories (Lincoln E. Moses, John D. Emerson, and Hossein Hosseini). SECTION IV: COMMUNICATING RESULTS. Chapter 14 Guidelines for Statistical Reporting in Articles for Medical Journals: Amplifications and Explanations (John C. Bailar III and Frederick Mosteller). Chapter 15 Reporting of Subgroup Analyses in Clinical Trials (Rui Wang, Stephen W. Lagakos, James H. Ware, David J. Hunter, and Jeffrey M. Drazen). Chapter 16 Writing about Numbers (Frederick Mosteller, Margaret Perkins, and Stephen Morrissey). SECTION V: SPECIALIZED METHODS. Chapter 17 Combining Results from Independent Studies: Systematic Reviews and Meta-Analysis in Clinical Research (Michael A. Stoto). Chapter 18 Biostatistics in Epidemiology: Advanced Methods of Regression Analysis (Mark S. Goldberg). Chapter 19 Genetic Inference (Dan L. Nicolae, Thorsten Kurz, and Carole Ober). Chapter 20 Identifying Disease Genes in Association Studies (Dan L. Nicolae, Thorsten Kurz, and Carole Ober). Chapter 21 Risk Assessment (A. John Bailer and John C. Bailar III). INDEX.

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Book SynopsisA unified treatment of the vulnerabilities that exist in real-world network systemswith tools to identify synergies for mergers and acquisitions Fragile Networks: Identifying Vulnerabilities and Synergies in an Uncertain World presents a comprehensive study of network systems and the roles these systems play in our everyday lives. This book successfully conceptualizes, defines, and constructs mathematically rigorous, computer-based tools for the assessment of network performance and efficiency, along with robustness and vulnerability analysis. The result is a thorough exploration that promotes an understanding of the critical infrastructure of today''s network systems, from congested urban transportation networks and supply chain networks under disruption to financial networks and the Internet. The authors approach the analyses by abstracting not only topological structures of networks, but also the behavior of network users, the demand for resources, the resulTable of ContentsPART I NETWORK FUNDAMENTALS, EFFICIENCY MEASUREMENT, AND VULNERABILITY ANALYSIS. 1 Introduction and Overview. 2 Fundamental Methodologies, Network Models, and Algorithms. 3 Network Performance Measurement and Robustness Analysis. PART II APPLICATIONS AND EXTENSIONS. 4 Application of the Measures to Transportation Networks. 5 Supply Chain Networks with Disruption Risks. 6 Critical Nodes and Links in Financial Networks. 7 Dynamic Networks, the Internet, and Electric Power. PART III MERGERS AND ACQUISITIONS, NETWORK INTEGRATION, AND SYNERGIES. 8 A System-Optimization Perspective for Supply Chain Network Integration. 9 Environmental and Cost Synergy in Network Integration. 10 Multiproduct Supply Chain Network Integration. 11 Network Oligopolies and the Merger Paradox. Appendix. Bibliography. Glossary.

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Book SynopsisThe purpose of this guide is to give a quick introduction on how to use Maple. It primarily covers Maple 12, although most of the guide will work with earlier versions of Maple. Also, throughout this guide, we will be suggesting tips and diagnosing common problems that users are likely to encounter. This should make the learning process smoother. This guide is designed as a self-study tutorial to learn Maple. Our emphasis is on getting you quickly ?up to speed.? This guide can also be used as a supplement (or reference) for students taking a mathematics (or science) course that requires use of Maple, such as Calculus, Multivariable Calculus, Advanced Calculus, Linear Algebra, Discrete Mathematics, Modeling, or Statistics.Table of ContentsPart I Basic Maple Commands Chapter 1 Running Maple 1 Computer Systems A Quick Tour More Examples Useful Tips Troubleshooting Q & A Chapter 2 Maple Input Modes 12 2-D Input Mode 1-D Input Mode Comparison of 1-D and 2-D Input Modes Useful Tips Troubleshooting Q & A Chapter 3 Calculator Features 19 Simple Arithmetic Output Styles Built-in Constants and Functions Error Messages More Examples Useful Tips Troubleshooting Q & A Chapter 4 Variables and Functions 27 Variables Functions More Examples Useful Tips Troubleshooting Q & A Chapter 5 Computer Algebra 34 Working with Polynomials and Powers Working with Rational, Functions Working with Trigonometric and Hyperbolic Functions Useful Tips Troubleshooting Q & A Chapter 6 Working with Equations 39 Equations and Their Solutions Numerical Solutions for Equations Guiding fsolve with a Range More Examples Useful Tips Troubleshooting Q & A Chapter 7 Sets, Lists, and Sequences 48 Lists and Sets Sequences More Examples Troubleshooting Q & A Chapter 8 Getting Help and Loading Packages 52 Getting Help Packages Useful Tips Part II Drawing Pictures in Maple Chapter 9 Making 2-D Pictures 55 Drawing the Graph of a Function More Options with the plot Command More Advanced Drawings Annotating a Plot with Text and Drawings More Examples Useful Tips Troubleshooting Q & A Chapter 10 Plotting Parametric Curves, Line Segments, and Points 64 Parametric Curves Plotting Line Segments and Polygonal Paths Plotting Multiple Curves in One Plot Useful Tips Troubleshooting Q & A Chapter 11 Polar and Implicit Plots 69 Plotting in Polar Coordinates Plotting Graphs of Equations Useful Tips Troubleshooting Q & A Part III Maple for One Variable Calculus Chapter 12 Limits and Derivatives 74 Limits Differentiation More Examples Useful Tips Troubleshooting Q & A Chapter 13 Integration 81 Antidifferentiation Definite Integration Numeric Integration More Examples Useful Tips Troubleshooting Q & A Chapter 14 Series and Taylor Series 88 Series Taylor Series More Examples Troubleshooting Q & A Chapter 15 Analyzing Differential Equations 95 Symbolic Solution of ODE Numeric Solutions of ODE Graphical Analysis of ODE Systems of Ordinary ODE More Examples Useful Tips Troubleshooting Q & A Part IV Maple for Multivariable Calculus Chapter 16 Making Graphs in Space 103 Graphing Functions of Two Variables Specifying Options Surfaces in Cylindrical and Spherical Coordinates More Examples Useful Tips Troubleshooting Q & A Chapter 17 Level Curves and Level Surfaces 111 Level Curves in the Plane Level Surfaces in Space More Examples Troubleshooting Q & A Chapter 18 Partial Differentiation and Multiple Integration 117 Partial Derivatives Double and Triple Integrals More Examples Useful Tips Troubleshooting Q & A Part V Maple for Linear Algebra and Vector Calculus Chapter 19 Matrices and Vectors 125 Matrices Vectors Some Useful Matrix Commands More Examples Useful Tips Troubleshooting Q & A Chapter 20 Parametric Curves and Surfaces in Space 132 Parametric Curves in Space Parametric Surfaces in Space Plotting Multiple Curves and Surfaces Shading, Coloring, and Transparency More Examples Troubleshooting Q & A Chapter 21 Vector Fields 139 Drawing a Vector Field Vector Fields in Different Coordinate Systems Gradient, Curl, and Divergence Line and Flux Integrals More Examples Useful Tips Troubleshooting Q & A Part VI Using Maple in Statistics Chapter 22 Basic Statistics on a Data Sample 150 Numerical Measures of Data Visualizing a Data Set Probability Distributions More Examples Useful Tips Chapter 23 Curve Fitting 156 Graphical Presentation of Data Regression Interpolation More Examples Useful Tips Troubleshooting Q & A Part VII Advanced Features of Maple Chapter 24 Animation 165 Getting Started Advanced Examples Useful Tips Troubleshooting Q & A Chapter 25 More About Lists 171 List Basics Useful List Commands More Examples Useful Tips Chapter 26 Random Numbers and Simulation 180 Random Numbers Examples in Simulation Other Distributions Useful Tips Troubleshooting Q & A Chapter 27 Maple for Programmers 189 Elements of Traditional Programming Languages Recursive Structures in Maple Viewing the Code of Maple Procedures File I/O Translating Maple into Other Programming Languages Appendices and Index A Learning Calculus with Maple 199 Maple Resources on the WWW The Student [Calculus1] Package B Quick Reference Guide 203 Mathematical Operations, Constants, and Functions Symbols and Abbreviations Some Frequently Used Commands Index 205

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Book SynopsisThis new edition exposes readers to a wide range of modern and technological applications, emphasizes proof throughout, and provides ample opportunities for practicing the presented concepts through homework exercises and worked examples.Table of ContentsPreface xiii Acknowledgments xx To The Student xxii 1 Introduction 1 1.1 What Is Discrete Mathematics? 1 1.1.1 A Break from the Past 3 1.2 The Stable Marriage Problem 3 1.2.1 Seeking a Solution 4 1.2.2 The Deferred Acceptance Algorithm 5 1.2.3 Some Concluding Comments 7 1.3 Other Examples 7 1.3.1 A Simple Counting and Probability Example 7 1.3.2 Sierpinski Curves 8 1.3.3 The Bridges of Konigsberg 9 1.3.4 Kirkman’s Schoolgirls 9 1.3.5 Finite-State Machines 10 1.3.6 The Set of Rational Numbers Is Countably Infinite 11 1.4 Exercises 13 1.5 Chapter Review 15 1.5.1 Summary 15 1.5.2 Notation 15 2 Sets, Logic, and Boolean Algebras 17 2.1 Sets 19 2.1.1 Definitions and Notation 19 2.1.2 Exercises 26 2.1.3 Proofs about Sets 29 2.1.4 Exercises 33 2.2 Logic in Daily Life 34 2.2.1 General Guidelines for Analyzing Claims 34 2.2.2 Informal Fallacies 35 2.2.3 Everyday Logic versus Symbolic Logic 37 2.2.4 Exercises 37 2.3 Propositional Logic 38 2.3.1 Truth Tables 39 2.3.2 The Operators NOT, AND, OR, and XOR 39 2.3.3 Negations of AND, OR, and NOT 40 2.3.4 Exercises 42 2.3.5 Implication and the Biconditional 43 2.3.6 Operator Precedence 46 2.3.7 Logical Equivalence 46 2.3.8 Derived Implications 47 2.3.9 Exercises 48 2.4 Logical Equivalence and Rules of Inference 50 2.4.1 Important Logical Equivalences and Rules of Inference 53 2.4.2 Proving that a Statement is a Tautology 54 2.4.3 Exercises 56 2.5 Boolean Algebras 58 2.5.1 Sets and Propositions as Boolean Algebras 60 2.5.2 Proving Additional Boolean Algebra Properties 63 2.5.3 Exercises 67 2.6 Predicate Logic 68 2.6.1 Quantifiers 69 2.6.2 Exercises 74 2.7 Quick Check Solutions 76 2.8 Chapter Review 81 2.8.1 Summary 81 2.8.2 Notation 82 2.8.3 Fundamental Properties 83 2.8.4 Additional Review Material 84 3 Proof 85 3.1 Introduction to Mathematical Proof 85 3.1.1 Mathematics and Proof: The Big Picture 86 3.1.2 Mathematical Objects Related to Proofs 87 3.1.3 Exercises 91 3.2 Elementary Number Theory: Fuel for Practice 92 3.2.1 The Integers and Other Number Systems 92 3.2.2 Divisibility 93 3.2.3 Primes 95 3.2.4 The Well-Ordering Principle 96 3.2.5 Congruence, Factorials, Floor and Ceiling Functions 98 3.2.6 Exercises 99 3.3 Proof Strategies 100 3.3.1 Trivial Proof 100 3.3.2 Direct Proof 101 3.3.3 Indirect Proof: Proving the Contrapositive 103 3.3.4 Proof by Contradiction 103 3.3.5 Proof by Cases 105 3.3.6 Implications with Existential Quantifiers 105 3.3.7 Implications with Universal Quantifiers 106 3.3.8 Proofs Involving the Biconditional and Logical Equivalence 108 3.3.9 Some Important Examples 109 3.3.10 Exercises 111 3.4 Applications of Elementary Number Theory 113 3.4.1 The Euclidean Algorithm: Calculating gcd(a, b) 113 3.4.2 Hashing 116 3.4.3 Pseudorandom Numbers 117 3.4.4 Linear Congruence and the Chinese Remainder Theorem 119 3.4.5 Fermat’s Little Theorem and Fermat’s Last Theorem 124 3.4.6 Encryption 126 3.4.7 Exercises 130 3.5 Mathematical Induction 132 3.5.1 The Principle of Mathematical Induction 132 3.5.2 Complete Induction 139 3.5.3 Interesting Mathematical Induction Problems 141 3.5.4 The Well-Ordering Principle, Mathematical Induction, and Complete Induction 146 3.5.5 Multidimensional Induction 148 3.5.6 Exercises 151 3.6 Creating Proofs: Hints and Suggestions 153 3.6.1 A Few Very General Suggestions 153 3.6.2 Some Specific Tactics 156 3.6.3 Exercises 161 3.7 Quick Check Solutions 162 3.8 Chapter Review 167 3.8.1 Summary 167 3.8.2 Notation 168 3.8.3 Additional Review Material 168 4 Algorithms 169 4.1 Expressing Algorithms 170 4.1.1 Flow of Control 170 4.1.2 Flow of Information 176 4.1.3 Exercises 179 4.2 Measuring Algorithm Efficiency 180 4.2.1 Big-2 and Its Cousins 181 4.2.2 Practical Big-2 Tools 185 4.2.3 Exercises 193 4.2.4 Big-2 in Action: Searching a List 195 4.2.5 Exercises 200 4.3 Pattern Matching 202 4.3.1 The Obvious Algorithm 202 4.3.2 KMP: Knuth–Morris–Pratt 204 4.3.3 BM: Boyer–Moore 206 4.3.4 Exercises 213 4.4 The Halting Problem 214 4.4.1 Setting the Stage 214 4.4.2 The Halting Problem 215 4.5 Quick Check Solutions 217 4.6 Chapter Review 222 4.6.1 Summary 222 4.6.2 Notation 223 4.6.3 Big-2 Shortcuts 224 4.6.4 Additional Review Material 224 5 Counting 225 5.1 Permutations and Combinations 226 5.1.1 Two Basic Counting Principles 226 5.1.2 Permutations 229 5.1.3 Permutations with Repetition 231 5.1.4 Combinations 231 5.1.5 Combinations with Repetition 234 5.1.6 Exercises 237 5.1.7 More Complex Counting Problems 239 5.1.8 Exercises 246 5.2 Combinatorial Proofs 248 5.2.1 Introduction to Combinatorial Proofs 248 5.2.2 Counting Tulips: Three Combinatorial Proofs 251 5.2.3 Exercises 257 5.3 Pigeon-Hole Principle; Inclusion–Exclusion 258 5.3.1 The Pigeon-Hole Principle 258 5.3.2 Inclusion–Exclusion 261 5.3.3 Exercises 264 5.4 Quick Check Solutions 266 5.5 Chapter Review 270 5.5.1 Summary 270 5.5.2 Notation 271 5.5.3 Some Counting Formulas 272 5.5.4 Additional Review Material 272 6 Finite Probability Theory 273 6.1 The Language of Probabilities 274 6.1.1 Sample Spaces, Outcomes, and Events 274 6.1.2 Probabilities of Events 277 6.1.3 Exercises 281 6.2 Conditional Probabilities and Independent Events 283 6.2.1 Definitions 283 6.2.2 Computing Probabilities 287 6.2.3 Exercises 294 6.3 Counting and Probability 297 6.3.1 Exercises 299 6.4 Expected Value 302 6.4.1 Exercises 308 6.5 The Binomial Distribution 310 6.5.1 Exercises 315 6.6 Bayes’s Theorem 316 6.6.1 Exercises 319 6.7 Quick Check Solutions 322 6.8 Chapter Review 327 6.8.1 Summary 327 6.8.2 Notation 328 6.8.3 Additional Review Material 328 7 Recursion 329 7.1 Recursive Algorithms 332 7.1.1 General Guidelines for Creating Recursive Algorithms 333 7.1.2 A Detailed Example 334 7.1.3 When Should Recursion Be Avoided? 336 7.1.4 Persian Rugs 339 7.1.5 Drawing Sierpinski Curves 342 7.1.6 Adaptive Quadrature 345 7.1.7 Exercises 349 7.2 Recurrence Relations 350 7.2.1 Solving Recurrence Relations 353 7.2.2 Linear Homogeneous Recurrence Relations with Constant Coefficients 357 7.2.3 Repeated Roots 366 7.2.4 The Sordid Truth 373 7.2.5 Exercises 375 7.3 Big-2 and Recursive Algorithms: The Master Theorem 377 7.3.1 Exercises 389 7.4 Generating Functions 391 7.4.1 Exercises 401 7.5 The Josephus Problem 402 7.5.1 Exercises 407 7.6 Quick Check Solutions 407 7.7 Chapter Review 414 7.7.1 Summary 414 7.7.2 Notation 416 7.7.3 Generating Function Table 416 7.7.4 Additional Review Material 416 8 Combinatorics 417 8.1 Partitions, Occupancy Problems, Stirling Numbers 419 8.1.1 Partitions of a Positive Integer 419 8.1.2 Occupancy Problems 423 8.1.3 Stirling Numbers 427 8.1.4 Exercises 433 8.2 Latin Squares, Finite Projective Planes 435 8.2.1 Latin Squares 435 8.2.2 Finite Projective Planes 442 8.2.3 Finite Projective Planes and Latin Squares 447 8.2.4 Exercises 457 8.3 Balanced Incomplete Block Designs 460 8.3.1 Constructing Balanced Incomplete Block Designs 464 8.3.2 Exercises 471 8.4 The Knapsack Problem 472 8.4.1 Exercises 485 8.5 Error-Correcting Codes 488 8.5.1 The 7-Bit Hamming Code 489 8.5.2 A Formal Look at Coding Theory 492 8.5.3 Combinatorial Aspects of Coding Theory 497 8.5.4 Exercises 500 8.6 Distinct Representatives, Ramsey Numbers 502 8.6.1 Systems of Distinct Representatives 502 8.6.2 Ramsey Numbers 509 8.6.3 Exercises 516 8.7 Quick Check Solutions 518 8.8 Chapter Review 529 8.8.1 Summary 529 8.8.2 Notation 531 8.8.3 The Fano Plane 532 8.8.4 Occupancy Problems 532 8.8.5 Additional Review Material 532 9 Formal Models in Computer Science 533 9.1 Information 533 9.1.1 A General Model of Communication 534 9.1.2 A Mathematical Definition of Information 535 9.1.3 A Summary of Other Ideas in Shannon’s Paper 540 9.1.4 Exercises 541 9.2 Finite-State Machines 542 9.2.1 Finite Automata 543 9.2.2 Finite-State Machines with Output 547 9.2.3 Exercises 551 9.3 Formal Languages 553 9.3.1 Regular Grammars 554 9.3.2 Exercises 559 9.4 Regular Expressions 560 9.4.1 Introduction to Regular Expressions 560 9.4.2 Perl Extensions 566 9.4.3 Exercises 568 9.5 The Three Faces of Regular 569 9.5.1 Optional: Completing the Proof of Kleene’s Theorem 576 9.5.2 Exercises 582 9.6 A Glimpse at More Advanced Topics 584 9.6.1 Context-Free Languages and Grammars 584 9.6.2 Turing Machines 585 9.6.3 Exercises 590 9.7 Quick Check Solutions 591 9.8 Chapter Review 596 9.8.1 Summary 596 9.8.2 Notation 597 9.8.3 Additional Review Material 598 10 Graphs 599 10.1 Terminology 600 10.1.1 New Graphs from Old 603 10.1.2 Special Graph Families 605 10.1.3 Exercises 608 10.2 Connectivity and Adjacency 609 10.2.1 Connectivity 609 10.2.2 The Adjacency Matrix 613 10.2.3 Exercises 615 10.3 Euler and Hamilton 618 10.3.1 Euler Circuits and Euler Trails 618 10.3.2 Hamilton Cycles and Hamilton Paths 620 10.3.3 Exercises 624 10.4 Representation and Isomorphism 626 10.4.1 Representation 626 10.4.2 Isomorphism 629 10.4.3 Exercises 631 10.5 The Big Theorems: Planarity, Euler, Polyhedra, Chromatic Number 634 10.5.1 Planarity 634 10.5.2 The Regular Polyhedra 639 10.5.3 Chromatic Number 642 10.5.4 Exercises 648 10.6 Directed Graphs and Weighted Graphs 651 10.6.1 Directed Graphs 651 10.6.2 Weighted Graphs and Shortest Paths 655 10.6.3 Exercises 662 10.7 Quick Check Solutions 665 10.8 Chapter Review 670 10.8.1 Summary 670 10.8.2 Notation 671 10.8.3 Additional Review Material 671 11 Trees 673 11.1 Terminology, Counting 673 11.1.1 Exercises 680 11.2 Traversal, Searching, and Sorting 682 11.2.1 Traversing Binary Trees 682 11.2.2 Binary Search Trees 685 11.2.3 Sorting 689 11.2.4 Exercises 690 11.3 More Applications of Trees 692 11.3.1 Parse Trees 692 11.3.2 Huffman Compression 694 11.3.3 XML 699 11.3.4 Exercises 708 11.4 Spanning Trees 711 11.4.1 Spanning Trees in Unweighted Graphs 711 11.4.2 Minimal Spanning Trees in Weighted Graphs 717 11.4.3 Exercises 722 11.5 Quick Check Solutions 726 11.6 Chapter Review 729 11.6.1 Summary 729 11.6.2 Notation 729 11.6.3 Additional Review Material 730 12 Functions, Relations, Databases, and Circuits 731 12.1 Functions and Relations 731 12.1.1 Functions 731 12.1.2 Relations 735 12.1.3 Exercises 737 12.2 Equivalence Relations, Partially Ordered Sets 739 12.2.1 Properties that Characterize Relations 739 12.2.2 Equivalence Relations and Partitions 742 12.2.3 Exercises 746 12.3 n-ary Relations and Relational Databases 748 12.3.1 n-ary Relations 748 12.3.2 Relational Databases 749 12.3.3 Functional Dependence; Models and Instances 751 12.3.4 Keys; Operations on Relations 752 12.3.5 Normal Forms 757 12.3.6 Exercises 769 12.4 Boolean Functions and Boolean Expressions 772 12.4.1 Boolean Functions 773 12.4.2 Binary Functions and Disjunctive Normal Form 775 12.4.3 Binary Expressions and Disjunctive Normal Form 778 12.4.4 Exercises 784 12.5 Combinatorial Circuits 785 12.5.1 Minimizing Binary Expressions 785 12.5.2 Combinatorial Circuits and Binary Expressions 789 12.5.3 Functional Completeness 793 12.5.4 Exercises 795 12.6 Quick Check Solutions 797 12.7 Chapter Review 805 12.7.1 Summary 805 12.7.2 Notation 806 12.7.3 Additional Review Material 806 A Number Systems A1 A.1 The Natural Numbers A1 A.2 The Integers A2 A.3 The Rational Numbers A2 A.4 The Real Numbers A4 A.5 The Complex Numbers A4 A.6 Other Number Systems A6 A.7 Representation of Numbers A7 B Summation Notation A10 C Logic Puzzles and Analyzing Claims A12 C.1 Logic Puzzles A12 C.1.1 Logic Puzzles about AND, OR, and NOT A12 C.1.2 Logic Puzzles about Implication, Biconditional, and Equivalence A16 C.1.3 Exercises A18 C.2 Analyzing Claims A18 C.2.1 Analyzing Claims that Contain Implications A18 C.2.2 Analyzing Claims that Contain Quantifiers A22 C.2.3 Exercises A23 C.3 Quick Check Solutions A24 D The Golden Ratio A27 E Matrices A29 F The Greek Alphabet A33 G Writing Mathematics A34 H Solutions to Selected Exercises A36 H.1 Introduction A36 H.2 Sets, Logic, and Boolean Algebras A36 H.3 Proof A42 H.4 Algorithms A47 H.5 Counting A51 H.6 Finite Probability Theory A54 H.7 Recursion A59 H.8 Combinatorics A63 H.9 Formal Models in Computer Science A68 H.10 Graphs A71 H.11 Trees A75 H.12 Functions, Relations, Databases, and Circuits A78 H.13 Appendices A83 Bibliography A85 Index A90

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Book SynopsisFinite Mathematics: An Applied Approach, 11th Edition once again lives up to its reputation as a clearly written, comprehensive finite mathematics book. This Edition builds upon a solid foundation by integrating new features and techniques that further enhance student interest and involvement. All existing problems have been updated to provide relevance and timeliness. Finite Mathematics contains the same elements such as Step-by-Step Examples, Exercise Sets, and Learning Objectives in every chapter. In an engaging and accessible style, this text demonstrates how mathematics applies to various fields of study. The text is packed with real data and real-life applications to business, economics, social and life sciences.Table of ContentsChapter 1: Linear Equations. 1.1. Lines. 1.2. Pairs of Lines. 1.3. Applications to Business and Economics. 1.4. Scatter Diagrams; Linear Curve Fitting. Chapter Review. Chapter Project. Chapter 2: Systems of Linear Equations. 2.1 Systems of Linear Equations: Substitution; Elimination. 2.2 Systems of Linear Equations: Gauss-Jordan Method. 2.3 Systems of m Linear Equations Containing n Variables. Chapter Review. Chapter Project. Chapter 3: Matrices. 3.1 Matrix Algebra. 3.2 Multiplication of Matrices. 3.3 The Inverse of a Matrix. 3.4 Applications in Economics (the Leontief Model), Accounting, and Statistics (the Method of Least Squares). Chapter Review. Chapter Project. Chapter 4: Linear Programming with Two Variables. 4.1 Systems of Linear Inequalities. 4.2 A Geometric Approach to Linear Programming Problems. Chapter Review. Chapter Project. Chapter 5: Linear Programming: Simplex Method. 5.1 The Simplex Tableau; Pivoting. 5.2 The Simplex Method; Solving Maximum Problems in Standard Form. 5.3 Solving Minimum Problems Using the Daily Principle. 5.4 The Simplex Method for Problems Not in Standard Form. Chapter Review. Chapter Project. Chapter 6: Finance. 6.1 Interest. 6.2 Compound Interest. 6.3 Annuities; Sinking Funds. 6.4 Present Value of an Annuity; Amortization. 6.5 Annuities and Amortization Using Recursive Sequences. Chapter Review. Chapter Project. Chapter 7: Probability. 7.1 Sets. 7.2 The Number of Elements in a Set. 7.3 The Multiplication Principle. 7.4 Sample Spaces and the Assignment of Probabilities. 7.5 Properties of the Probability of an Event. 7.6 Expected Value. Chapter Review. Chapter Project. Chapter 8: Bayes' Theorem; The Binomial Probability Model. 8.1 Conditional Probability. 8.2 Independent Events. 8.3 Bayes' Theorem. 8.4 Permutations. 8.5 Combinations. 8.6 The Binomial Probability Model. Chapter Review. Chapter Project. Chapter 9: Statistics. 9.1 Introduction to Statistics: Data and Sampling. 9.2 Representing Qualitative Data Graphically: Bar Graphs; Pie Charts. 9.3 Organizing and Displaying Quantitative Data. 9.4 Measures of Central Tendency. 9.5 Measures of Dispersion. 9.6 The Normal Distribution. Chapter Review. Chapter Project. Chapter 10: Markov Chains; Games. 10.1 Markov Chains and Transition Matrices. 10.2 Regular Markov Chains. 10.3 Absorbing Markov Chains. 10.4 Two-Person Games. 10.5 Mixed Strategies. 10.6 Optimal Strategy in Two-Person Zero-Sum Games with 2 X 2 Matrices. Chapter Review. Chapter Project. Chapter 11: Logic. 11.1 Propositions. 11.2 Truth Tables. 11.3 Implications; The Biconditional Connective; Tautologies. 11.4 Arguments. 11.5 Logic Circuits. Chapter Review. Chapter Project. Appendix A: Review. A.1 Real Numbers. A.2 Algebra Essentials. A.3 Exponents and Logarithms. A.4 Recursive Defined Sequences: Geometric Sequences. Appendix B: Using LINDO to Solve Linear Programming Problems. Appendix C: Graphing Utilities. C.1 The Viewing Rectangle. C.2 Using a Graphing Utility to Graph Equations. C.3 Square Screens. C.4 Using a Graphing Utility to Graph Inequalities. Answers to Odd-Numbered Problems. Photo Credits. Index.

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Book SynopsisThe new 6th edition of Applied Combinatorics builds on the previous editions with more in depth analysis of computer systems in order to help develop proficiency in basic discrete math problem solving. As one of the most widely used book in combinatorial problems, this edition explains how to reason and model combinatorically while stressing the systematic analysis of different possibilities, exploration of the logical structure of a problem, and ingenuity. Although important uses of combinatorics in computer science, operations research, and finite probability are mentioned, these applications are often used solely for motivation. Numerical examples involving the same concepts use more interesting settings such as poker probabilities or logical games. This book is designed for use by students with a wide range of ability and maturity (sophomores through beginning graduate students). The stronger the students, the harder the exercises that can be assigned. The booTable of ContentsPrelude xi Part One Graph Theory 1 Chapter 1 Elements of Graph Theory 3 1.1 Graph Models 3 1.2 Isomorphism 14 1.3 Edge Counting 24 1.4 Planar Graphs 31 1.5 Summary and References 44 Supplementary Exercises 45 Chapter 2 Covering Circuits and Graph Coloring 49 2.1 Euler Cycles 49 2.2 Hamilton Circuits 56 2.3 Graph Coloring 68 2.4 Coloring Theorems 77 2.5 Summary and References 86 Supplement: Graph Model for Instant Insanity 87 Supplement Exercises 92 Chapter 3 Trees and Searching 93 3.1 Properties of Trees 93 3.2 Search Trees and Spanning Trees 103 3.3 The Traveling Salesperson Problem 113 3.4 Tree Analysis of Sorting Algorithms 121 3.5 Summary and References 125 Chapter 4 Network Algorithms 127 4.1 Shortest Paths 127 4.2 Minimum Spanning Trees 131 4.3 Network Flows 135 4.4 Algorithmic Matching 153 4.5 The Transportation Problem 164 4.6 Summary and References 174 Part Two Enumeration 177 Chapter 5 General Counting Methods for Arrangements and Selections 179 5.1 Two Basic Counting Principles 179 5.2 Simple Arrangements and Selections 189 5.3 Arrangements and Selections with Repetitions 206 5.4 Distributions 214 5.5 Binomial Identities 226 5.6 Summary and References 236 Supplement: Selected Solutions to Problems in Chapter 5 237 Chapter 6 Generating Functions 249 6.1 Generating Function Models 249 6.2 Calculating Coefficients of Generating Functions 256 6.3 Partitions 266 6.4 Exponential Generating Functions 271 6.5 A Summation Method 277 6.6 Summary and References 281 Chapter 7 Recurrence Relations 283 7.1 Recurrence Relation Models 283 7.2 Divide-and-Conquer Relations 296 7.3 Solution of Linear Recurrence Relations 300 7.4 Solution of Inhomogeneous Recurrence Relations 304 7.5 Solutions with Generating Functions 308 7.6 Summary and References 316 Chapter 8 Inclusion–Exclusion 319 8.1 Counting with Venn Diagrams 319 8.2 Inclusion–Exclusion Formula 328 8.3 Restricted Positions and Rook Polynomials 340 8.4 Summary and Reference 351 Part Three Additional Topics 353 Chapter 9 Polya’s Enumeration Formula 355 9.1 Equivalence and Symmetry Groups 355 9.2 Burnside’s Theorem 363 9.3 The Cycle Index 369 9.4 Polya’s Formula 375 9.5 Summary and References 382 Chapter 10 Games with Graphs 385 10.1 Progressively Finite Games 385 10.2 Nim-Type Games 393 10.3 Summary and References 400 Postlude 401 Appendix 415 A.1 Set Theory 415 A.2 Mathematical Induction 420 A.3 A Little Probability 423 A.4 The Pigeonhole Principle 427 A.5 Computational Complexity and NP-Completeness 430 Glossary of Counting and Graph Theory Terms 435 Bibliography 439 Solutions To Odd-Numbered Problems 441 Index 475

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Book SynopsisDesign, Execution, and Management of Medical Device Clinical Trials provides practical examples, ranging from the development of clinical protocol and the selection of clinical sites and ending with the completion of the final clinical report study.Table of ContentsList of Abbreviations xi Preface xv Acknowledgments xxi 1 An Overview of Clinical Study Tasks and Activities 1 Key Clinical Study Tasks and Activities 2 Discussion of Key Tasks and Activities 3 Management of Key Clinical Tasks and Activities 9 Example of the Spread Sheet for Managing Clinical Study Activities 10 The Clinical Research Team 10 2 Development of Clinical Protocols, Case Report Forms, Clinical Standard Operating Procedures, Informed Consent Form, Study Regulatory Binder, Study Research Agreement, and Other Clinical Materials 15 Clinical Protocol 17 Case Report Forms (CRFs) 28 Example of the Case Report Form Template 32 Informed Consent Form (ICF) 35 Instructions for Use of Device 38 Study Regulatory Binder 39 Study Research Agreement 40 Research Agreement Template 43 Research Contract Challenges 50 Clinical Forms and Certificates 51 Clinical Standard Operating Procedures (SOPs) 54 3 Qualification/Selection of Study Investigators and Study Monitoring Visits 63 Qualification and Selection of Investigators 64 Monitoring Visits 67 Monitoring Reports 73 Interim Monitoring Visit Report Template 75 4 Adverse Events Definitions and Reporting Procedures 81 Adverse Event Definitions 83 Policies, Regulations, and Guidelines Regarding Adverse Event Reporting 84 Adverse Event Reporting Pathway 84 Terms for Causality Assessment 85 GAPS/Challenges in Adverse Event Reporting 86 Adverse Event Reporting Time Periods (21 CFR 803) 88 Differences between the United States and Europe in Reporting Adverse Events 88 Serious Adverse Event Narratives 89 Classification of Adverse Events 90 Special Requirement for Reporting Certain Adverse Events 92 Case Example 92 Mandatory Device Reporting for FDA-Approved Devices 92 5 Statistical Analysis Plan (SAP) and Biostatistics in Clinical Research 93 Statistical Analysis Plan (SAP) 94 Selection of Study Endpoints 97 Biostatistics in Clinical Research 101 6 Final Clinical Study Report 113 Final Clinical Report’s Outline 114 Discussion of Sections in the Final Clinical Report 116 7 Medical Device Regulations Combination Product, Study Committees, and FDA-Sponsor Meetings 127 Medical Device Regulations 129 Combination Products 164 Study Committees 168 FDA-Sponsor Meetings 170 Registration of Clinical Trials 174 Implementation of the HIPAA Privacy Rule in Clinical Research 175 Institutional Review Boards (IRB) 180 FDA’s Oversight of Clinical Trials (Bioresearch Monitoring) 191 Code of Federal Regulations of Medical Devices 194 8 Design Issues in Medical Devices Studies 195 Design of the Clinical Trial 196 Assumptions and Parameters of Clinical Trial Design 196 Clinical Trials’ Design Issues and Data Analysis Issues 202 Use of Historic Controls as the Control Group in IDE Studies 206 Summary of Recommendations When Using Historic Controls 218 9 Investigator-Initiated Clinical Research 221 Definition and Examples of Investigator-Initiated Clinical Research 222 Development, Conduct, and Management of Investigator-Initiated Clinical Research 224 Regulation of Investigator-Initiated Clinical Research 225 Required Infrastructure for Investigator-Initiated Clinical Research 226 Clinical Research Sponsored by NIH 227 10 Ethical Conduct for Human Research 229 The Nuremberg Code (1947) 230 World Medical Association—Declaration of Helsinki (1964–Present) 231 National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (1974) 231 The Belmont Report (1978) 232 Special Ethical Concerns in Clinical Research on Use of Placebo 232 Glossary of Clinical Trial and Statistical Terms 235 References 249 Index 255

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Book Synopsisone-of-a-kind introduction to the theory and application of modeling and simulation techniques in the realm of international studies Modeling and Simulation for Analyzing Global Events provides an orientation to the theory and application of modeling and simulation techniques in social science disciplines.Table of ContentsPreface. I PRINCIPLES OF MODELING AND SIMULATION: ADVANCING GLOBAL STUDIES. 1 Modeling and Simulation: What, When, and Why. Introduction. An Overview of Modeling and Simulation. A Brief History of Modeling and Simulation. Why Use Modeling and Simulation. Conclusions. Key Terms. References. Further Reading. 2 Research Methodologies for Modeling Global Events. Introduction. Global Events and the Social Sciences. Qualitative and Quantitative Research. Modeling and Simulation of Global Events. Mapping Data: A Suggested Methodology. Model Validation. Conclusions. Key Terms. References. II MODELING PARADIGMS. 3 System Dynamics. Introduction. Dynamic System Behavior. Building Blocks of System Dynamics Models. Conclusions. Key Terms. References. 4 Agent-Based Modeling and Social Networks. Introduction. Agent-Based Models: Description and Definition. Social Networks. Building an Agent-Based Model. Conclusions. Key Terms. References. 5 Game Theory. Introduction. Fundamentals of Game Theory. Types of Games. Conclusions. Key Terms. References. III MODELING GLOBAL EVENTS. 6 Case Study: Colombia—A Country Study on Insurgency. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Mapping Qualitative to Quantitative. System Dynamics. Responding to the Research Question. Key Terms. References. Case Study Bibliography. 7 Case Study: The Polish Solidarity Movement—Laying the Foundation for the Collapse of Soviet Communism. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Measuring Agents and Environments: Stimuli and Actions. Modeling Human Behavior with Agents. Responding to the Research Question. Conclusions. Key Terms. References. Case Study Bibliography. 8 Case Study: Vietnam—Johnson’s War, 1963–1965. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Analyzing the Social Network Structures. Social Network Aspects of Human Behavior Modeling. Agent-Based Model Development. Responding to the Research Question. Key Terms. References. Case Study Bibliography. 9 Case Study: Cuban Missile Crisis—A National Security Emergency. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Evaluating Behaviors. Game Theory. Responding to the Research Question. Key Terms. References. Case Study Bibliography. Index.

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Book SynopsisSurveying Cultures uniquely employs techniques rooted in survey methodology to discover cultural patterns in social science research. Examining both classical and emerging methods that are used to survey and assess differing norms among populations, the book successfully breaks new ground in the field, introducing a theory of measurement for ethnographic studies that employs the consensus-as-culture model. The book begins with a basic overview of cross-cultural measurement of sentiments and presents innovative and sophisticated analyses of measurement issues and of homogeneity among respondents. Subsequent chapters explore topics that are at the core of successful data collection and analysis in culture studies, including: The role of bipolar scales and Internet data collection in measuring sentiments Key methodological variables that determine the quality of quantitative data, including measurement errors, validity, and reliability Table of ContentsPreface ix Acknowledgments xi 1 Surveying Culture 1 1.1 Case Studies of Cultural Surveys 3 1.2 Preview 18 1.3 Chapter Highlights 20 2 Measuring Sentiments 23 2.1 Dimensions of Affect 26 2.2 Bipolar Scales 35 2.3 Internet Data Collection 51 2.4 Chapter Highlights 54 3 Sentiment Repositories 57 3.1 Early Archives 58 3.2 Cross-Cultural Atlas 60 3.3 Archives Related to Social Interaction 61 3.4 U.S. 2002–2004 Project 67 3.5 Chapter Highlights 73 4 Surveys with Vignettes 75 4.1 Factorial Surveys 76 4.2 Impressions from Events 86 4.3 Attribute–Identity Amalgamations 98 4.4 Event Likelihoods 101 4.5 Synopsis 106 4.6 Chapter Highlights 108 4.7 Appendix: Impression-Formation Study Designs 109 5 Errors in Surveys 121 5.1 Coverage Errors 122 5.2 Sampling Errors 124 5.3 Nonresponse Errors 124 5.4 Measurement Errors 125 5.5 Other Errors 129 5.6 A Survey-of-Cultures Model 129 5.7 Statistics 135 5.8 Inculcation Index 139 5.9 Commonality Index 141 5.10 Variance Components 142 5.11 Implications 144 5.12 Chapter Highlights 146 6 Correlates of Enculturation 149 6.1 Indices 150 6.2 Conduct as a Rater 151 6.3 Predicting Cultural Authoritativeness 153 6.4 Implications 160 6.5 Chapter Highlights 164 7 Consensus in Sentiments 167 7.1 Component Analyses 168 7.2 Subcultures 176 7.3 Discussion 179 7.4 Chapter Highlights 180 8 Measurement Reliability 183 8.1 Reliabilities Within Stimuli 184 8.2 Reliabilities Across Stimuli 194 8.3 Chapter Highlights 200 9 Culture and Surveys 203 9.1 Unique Aspects of Sentiment Surveys 203 9.2 Frameworks for Sentiment Surveys 207 9.3 In Closing 210 9.4 Chapter Highlights 211 References 213 Index 223

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Book SynopsisPraise for the First Edition . . . an excellent addition to an upper-level undergraduate course on environmental statistics, and . . . a ''must-have'' desk reference for environmental practitioners dealing with censored datasets. ?Vadose Zone Journal Statistics for Censored Environmental Data Using Minitab and R, Second Edition introduces and explains methods for analyzing and interpreting censored data in the environmental sciences. Adapting survival analysis techniques from other fields, the book translates well-established methods from other disciplines into new solutions for environmental studies. This new edition applies methods of survival analysis, including methods for interval-censored data to the interpretation of low-level contaminants in environmental sciences and occupational health. Now incorporating the freely available R software as well as Minitab into the discussed analyses, the book features neTrade Review“Helsel’s book is an excellent resource for scientists and statisticians, as well as an effective textbook for advanced undergraduate and graduate school students.” (Integrated Environmental Assessment and Management, 1 May 2014)Table of ContentsPreface ix Acknowledgments xi Introduction to the First Edition: An Accident Waiting to Happen xiii Introduction to the Second Edition: Invasive Data xvii 1 Things People Do with Censored Data that Are Just Wrong 1 Why Not Substitute—Missing the Signals that Are Present in the Data 3 Why Not Substitute?—Finding Signals that Are Not There 8 So Why Not Substitute? 10 Other Common Misuses of Censored Data 10 2 Three Approaches for Censored Data 12 Approach 1: Nonparametric Methods after Censoring at the Highest Reporting Limit 13 Approach 2: Maximum Likelihood Estimation 14 Approach 3: Nonparametric Survival Analysis Methods 17 Application of Survival Analysis Methods to Environmental Data 17 Parallels to Uncensored Methods 21 3 Reporting Limits 22 Limits When the Standard Deviation is Considered Constant 23 Insider Censoring–Biasing Interpretations 29 Reporting the Machine Readings of all Measurements 33 Limits When the Standard Deviation Changes with Concentration 34 For Further Study 36 4 Reporting, Storing, and Using Censored Data 37 Reporting and Storing Censored Data 37 Using Interval-Censored Data 41 Exercises 42 5 Plotting Censored Data 44 Boxplots 44 Histograms 46 Empirical Distribution Function 47 Survival Function Plots 49 Probability Plot 52 X–Y Scatterplots 59 Exercises 61 6 Computing Summary Statistics and Totals 62 Nonparametric Methods after Censoring at the Highest Reporting Limit 62 Maximum Likelihood Estimation 64 The Nonparametric Kaplan–Meier and Turnbull Methods 70 ROS: A “Robust” Imputation Method 79 Methods in Excel 86 Handling Data with High Reporting Limits 86 A Review of Comparison Studies 87 Summing Data with Censored Observations 94 Exercises 98 7 Computing Interval Estimates 99 Parametric Intervals 100 Nonparametric Intervals 103 Intervals for Censored Data by Substitution 103 Intervals for Censored Data by Maximum Likelihood 104 Intervals for the Lognormal Distribution 112 Intervals Using “Robust” Parametric Methods 125 Nonparametric Intervals for Censored Data 126 Bootstrapped Intervals 136 For Further Study 140 Exercises 141 8 What Can be Done When All Data Are Below the Reporting Limit? 142 Point Estimates 143 Probability of Exceeding the Reporting Limit 144 Exceedance Probability for a Standard Higher than the Reporting Limit 148 Hypothesis Tests Between Groups 151 Summary 152 Exercises 152 9 Comparing Two Groups 153 Why Not Use Substitution? 154 Simple Nonparametric Methods After Censoring at the Highest Reporting Limit 156 Maximum Likelihood Estimation 161 Nonparametric Methods 167 Value of the Information in Censored Observations 178 Interval-Censored Score Tests: Testing Data that Include (DL to RL) Values 180 Paired Observations 183 Summary of Two-Sample Tests for Censored Data 192 Exercises 192 10 Comparing Three or More Groups 194 Substitution Does Not Work—Invasive Data 195 Nonparametric Methods after Censoring at the Highest Reporting Limit 196 Maximum Likelihood Estimation 199 Nonparametric Method—The Generalized Wilcoxon Test 209 Summary 215 Exercises 216 11 Correlation 218 Types of Correlation Coefficients 218 Nonparametric Methods after Censoring at the Highest Reporting Limit 219 Maximum Likelihood Correlation Coefficient 224 Nonparametric Correlation Coefficient—Kendall’s Tau 227 Interval-Censored Score Tests: Testing Correlation with (DL to RL) Values 230 Summary: A Comparison Among Methods 232 For Further Study 234 Exercises 235 12 Regression and Trends 236 Why Not Substitute? 237 Nonparametric Methods After Censoring at the Highest Reporting Limit 239 Maximum Likelihood Estimation 249 Akritas–Theil–Sen Nonparametric Regression 258 Additional Methods for Censored Regression 264 Exercises 266 13 Multivariate Methods for Censored Data 268 A Brief Overview of Multivariate Procedures 269 Nonparametric Methods After Censoring at the Highest Reporting Limit 273 Multivariate Methods for Data with Multiple Reporting Limits 288 Summary of Multivariate Methods for Censored Data 296 14 The NADA for R Software 297 A Brief Overview of R and the NADA Software 297 Summary of the Commands Available in NADA 300 Appendix: Datasets 303 References 309 Index 321

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Book SynopsisUniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations research: modeling real-world problems as linear optimization problem; designing the necessary algorithms to solve these problems; and using mathematical theory to justify algorithmic development. Treating real-world examples as mathematical problems, the author begins with an introduction to operations research and optimization modeling that includes applications form sports schedulinTrade Review“Dr. Phillips has used other texts, but he is especially enthused with this book, influenced by student feedback. He says, “Algorithmic ideas are introduced at a pace that emphasizes and encourages intuitive understanding.” (Informs Journal on Computing, 1 June 2012) "The book is aimed at serving upper-undergraduate and graduate students of all fields as a comprehensive textbook or as a reference for studies on the subject." (Zentralblatt MATH, 2011) "The result is a clear-cut resource for understanding three cornerstones of deterministic operations research: modeling real-world problems as linear optimization problems; designing the necessary algorithms to solve these problems; and using mathematical theory to justify algorithmic development." (InfoTECH Spotlight - TMCnet, 8 February 2011) Table of ContentsPreface. 1. Introduction to Operations Research. 1.1 What is Deterministic Operations Research? 1.2 Introduction to Optimization Modeling. 1.3 Common Classes of Mathematical Programs. 1.4 About the Book. Exercises. 2. Linear Programming Modeling. 2.1 Resource Allocation Models. 2.2 Work Scheduling Models. 2.3 Models and Data. 2.4 Blending Models. 2.5 Production Process Models. 2.6 Multiperiod Models: Work Scheduling and Inventory. 2.7 Linearization of Special Nonlinear Models. 2.8 Various Forms of Linear Programs. 2.9 Network Models. Exercises. 3. Integer and Combinatorial Models. 3.1 Fixed-Charge Models. 3.2 Set Covering Models. 3.3 Models Using Logical Constraints. 3.4 Combinatorial Models. 3.5 Sports Scheduling and an Introduction to IP Solution Technques. Exercises. 4. Real-World Operations Research Applications: An Introduction. 4.1 Vehicle Routing Problems. 4.2 Facility Location and Network Design Models. 4.3 Applications in the Airline Industry. Exercises. 5. Introduction to Algorithm. 5.1 Exact and Heuristic Algorithms. 5.2 What to Ask When Designing Algorithms? 5.3 Constructive versus Local Search Algorithms. 5.4 How Good is our Heuristic Solution? 5.5 Example of a Local Search Method. 5.7 Other Heuristic Methods. 5.8 Designing Exact Methods: Optimality Conditions. Exercises. 6. Improving Search Algorithms and Comvexity. 6.1 Improving Search and Optimal Solutions. 6.2 Finding Better Solutions. 6.3 Convexity: When Does Improving Search Imply Global Optimality? 6.4 Farkas’ Lemma: When Can No Improving Feasible Direction be Found? Exercises. 7. Geometry and Algebra of Linear Programs. 7.1 Geometry and Algebra of “Corner Points”. 7.2 Fundamental Theorem of Linear Programming. 7.3 Linear Programs in Canonical Form. Exercises. 8. Solving Linear Programs: Simplex Method. 8.1 Simplex Method. 8.2 Making the Simplex Method More Efficient. 8.3 Convergence, Degeneracy, and the Simplex Method. 8.4 Finding an Initial Solution: Two-Phase Method. 8.5 Bounded Simplex Method. 8.6 Computational Issues. Exercises. 9. Linear Programming Duality. 9.1 Motivation: Generation Bounds. 9.2 Dual Linear Program. 9.3 Duality Theorems. 9.4 Another Interpretation of the Simplex Method. 9.5 Farkas’ Lemma Revisited. 9.6 Economic Interpretation of the Dual. 9.7 Another Duality Approach: Lagrangian Duality. Exercises. 10. Sensitivity Analysis of Linear Programs. 10.1 Graphical Sensitivity Analysis. 10.2 Sensitivity Analysis Calculations. 10.3 Use of Sensitivity Analysis. 10.4 Parametric Programming. Exercises. 11. Algorithmic Applications of Duality. 11.1 Dual Simplex Method. 11.2 Transportation Problem. 11.3 Column Generation. 11.4 Dantzig-Wolfe Decomposition. 11.5 Primal-Dual Interior Point Method. Exercises. 12. Network Optimization Algorithms. 12.1 Introduction to Network Optimization. 12.2 Shortest Path Problems. 12.3 Maximum Flow Problems. 12.4 Minimum Cost Network Flow Problems. Exercises. 13. Introduction to Integer Programming. 13.1 Basic Definitions and Formulations. 13.2 Relaxations and Bounds. 13.3 Preprocessing and Probing. 13.4 When are Integer Programs “Easy?’ Exercises. 14. Solving Integer Programs: Exact Methods. 14.1 Complete Enumeration. 14.2 Branch-and Bound Methods. 14.3 Valid Inequalities and Cutting Planes. 14.4 Gomory’s Cutting Plane Algorithm. 14.5 Valid Inequalities for 0-1 Knapsack Constraints. 14.6 Branch-and-Cut Algorithms. 14.7 Computational Issues. Exercises. 15. Solving Integer Programs: Modern Heuristic Techniques. 15.1 Review of Local Search Methods: Pros and Cons. 15.2 Simulated Annealing. 15.3 Tabu Search. 15.4 Genetic Algorithms. 15.5 GRASP Algorithms. Exercises. Appendix A: Background Review. A.1 Basic Notation. A.2 Graph Theory. A.3 Linear Algebra. Reference. Index.

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Book SynopsisA guide to modeling analyses for financial and sports gambling markets, with a focus on major current events Addressing the highly competitive and risky environments of current-day financial and sports gambling markets, Forecasting in Financial and Sports Gambling Markets details the dynamic process of constructing effective forecasting rules based on both graphical patterns and adaptive drift modeling (ADM) of cointegrated time series. The book uniquely identifies periods of inefficiency that these markets oscillate through and develops profitable forecasting models that capitalize on irrational behavior exhibited during these periods. Providing valuable insights based on the author''s firsthand experience, this book utilizes simple, yet unique, candlestick charts to identify optimal time periods in financial markets and optimal games in sports gambling markets for which forecasting models are likely to provide profitable trading and wagering outcomes. FeaturiTrade Review"This book is a valuable reference for researchers and practitioners in the area of finance. It is also useful for graduate students in financial economics and time series analysis as well as for anyone who wants to profit from financial and sports gambling markets." (Zentralblatt MATH, 2011)Table of ContentsPreface. 1.Introduction. 1.1 Favorable Betting Scenarios. 1.2 Gambling Shocks. 1.3 The Dark Side of Sports: The Fixes. 2. Market Perspectives: Through a Glass Darkly. 2.1 Changing Paradigms. 2.2 Modeling Commentaries. 2.3 Sports Hedge Funds. 2.4 Gambling Markets: Prohibition, Repeal and Taxation. 2.5 Quantifying the Madness of Crowds in Sports Gambling Markets. 2.6 Statistical Shocks: Alias Variables. 3. Opacity and Present Day Variables. 3.1 Dilemmas between Social and Economic Efficiency. 3.2 Towards a More Visible Hidden Hand. 3.3 Hedge Funds and Galapagos. 3.4 Lotteries: Market for Losers. 4. Adaptive Modeling Concepts in Dynamic Markets. 4.1 Quant Funds and Algorithmic Trading. 4.2 Market Volatility and Fat-Trailed Distributions. 4.3 Adaptive ARMA(1,1) Drift Processes. 4.4 Time Varying Volatility. 5. Studies in Japanese Candlestick Charts. 5.1 Bullish and Bearish Patterns from Chartist Perspectives. 5.2 Black Monday. 5.3 A Matter of Alleged Insider Trading. 5.4 Commodity Bubbles and Volatility. 5.5 Short Selling. 5.6 Terrorist Attacks and the Markets. 5.7 A Hollywood Romance: Spiderman and Tinkerbell. 5.8 Copenhagen and Climate Change: Exxon Mobile Buys XTO Energy. 6. Pseudo Candlestick Graphics for Major League Baseball. 6.1 The 2008 World Series: Philadelphia Versus Tampa Bay. 6.2 The 2008 Chicago Cubs: Visions of 1908 Heroics. 6.3 A Strange Set of Coincidences: A Plate Umpire’s Affinity for a Pitcher. 7. Single Equation Adaptive Drift Modeling. 7.1 Adaptive ARMA Processes. 7.2 Variable Selection: Identifying the Reduced Model. 7.3 Reduced Model Estimation: Single Equations. 7.4 Reduced Model Empirical Bayesian Estimation: Single Equations. 7.5 Single Equation Volatility Modeling: Adaptive GARCH Processes. 7.6 Modeling Monetary Growth Data. 7.7 Modeling GNP Deflator Growth. 8. Single Equation Modeling: Sports Gambling Markets. 8.1 Effects of Interactive Gambling Shocks. 8.2 End of an Era: Modeling Profile of the 1988-89 Los Angeles Lakers. 8.3 Spread Betting. 8.4 Modeling Profile of a Dream Team: The 1989-90 San Francisco 49ers. 8.5 Major League Baseball: A Data Intensive Game. 8.6 While Still Under the Curse: Modeling Profile of the 1990 Red Sox. 8.7 Portrait of Controversy: Modeling Profile of Roger Clemens with the 1990 Red Sox. 8.8 Pitcher of the Year in 1990: Modeling Profile of the Oakland’s Bob Gibson. 9. Simultaneous Financial Time Series. 9.1 The Cruse of Higher Dimensionality. 9.2 From Candlesticks to Cointegration. 9.3 Cointegration in Terms of Autoregressive Processes. 9.4 Estimating Disequilibria through Factor Analysis. 9.5 Simultaneous Time Series: Adaptive Drift Modeling. 9.6 Simultaneous Time Series: Adaptive Volatility Modeling. 9.7 Exploratory Modeling: Marathon Oil Company. 9.8 The High Tech Bubble of 2000. 9.9 Twenty-Five Standard Deviation Moves. 9.10 The March 2009 Nadir. 10. Modeling Cointegrated Time Series Associated with NBA and NFL Games. 10.1 Modeling Transitions. 10.2 The 2007-08 NY Giants: As Unexpected as Katrina. 10.3 Misery for the Patriot Faithful. 10.4 The Pittsburgh Steelers in Super Bowl 2005. 10.6 Miami’s First NBA Title: 2005-06. 10.7 The 2006-07 San Antonio Spurs: Unexpected Titlists. 10.8 Monitoring MBA Referee Performances. 11. Categorical Forecasting. 11.1 Fisher’s Discriminant Function. 11.2 Bayesian Discriminant Analysis. 11.3 Logistic Regression Analysis. 11.4 Allocating Betting Monies in the Sports Gambling Markets. 12. Financial/Mathematical Illiteracy and Adolescent Problem Gambling: Epidemics More Damaging Than the 1918 Influenza Pandemic. 12.1 The Call for Financial/Mathematical Literacy in 21st Century America. 12.2 Data, Information and the Information Age. 12.3 The Companion Epidemic of Adolescent Problem Gambling. 12.4 Results of a Pilot Study on Adolescent Problem Gambling and Financial /Mathematical Literacy. 13. The Influenza Futures Markets. 13.1 Markets for Expert Information Retrieval. 13.2 Adaptive Seasonal Time Series Modeling. 13.3 Forecasting Weekly Influenza/Pneumonia Deaths. Index. List of Figures.

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Book SynopsisAn insightful presentation of the key concepts, paradigms, and applications of modeling and simulation Modeling and simulation has become an integral part of research and development across many fields of study, having evolved from a tool to a discipline in less than two decades.Trade Review"This text provides a well-designed overview of M&S as a discipline useful for graduate students with engineering, mathematics or computer science background and for specialists interested in fundamental principles of M&S, its further development and applications." (Zentralblatt MATH, 2010) Table of ContentsPreface. Contributors. 1 Introduction to Modeling and Simulation (Catherine M. Banks). M&S. M&S Characteristics and Descriptors. M&S Categories. Conclusion. References. 2 Statistical Concepts for Discrete Event Simulation (Roland R. Mielke). Probability. Simulation Basics. Input Data Modeling. Output Data Analysis. Conclusion. References. 3 Discrete-Event Simulation (Rafael Diaz and Joshua G. Behr). Queuing System Model Components. Simulation Methodology. DES Example. Hand Simulation—Spreadsheet Implementation. Arena Simulation. Conclusion. References. 4 Modeling Continuous Systems (Wesley N. Colley). System Class. Modeling and Simulation (M&S) Strategy. Modeling Approach. Model Examples. Simulating Continuous Systems. Simulation Implementation. Conclusion. References. 5 Monte Carlo Simulation (John A. Sokolowski). The Monte Carlo Method. Sensitivity Analysis. Conclusion. References. 6 Systems Modeling: Analysis and Operations Research (Frederic D. McKenziei). System Model Types. Modeling Methodologies and Tools. Analysis of Modeling and Simulation (M&S). OR Methods. Conclusion. References. Further Readings. 7 Visualization (Yuzhong Shen). Computer Graphics Fundamentals. Visualization Software and Tools. Case Studies. Conclusion. References. 8 M&S Methodologies: A Systems Approach to the Social Sciences (Barry G. Silverman, Gnana K. Bharathy, Benjamin Nye, G. Jiyun Kim, Mark Roddy, and Mjumbe Poe). Simulating State and Substate Actors with CountrySim: Synthesizing Theories Across the Social Sciences. The CountrySim Application and Sociocultural Game Results. Conclusions and the Way Forward. References. 9 Modeling Human Behavior (Yiannis Papelis and Poornima Madhavan). Behavioral Modeling at the Physical Level. Behavioral Modeling at the Tactical and Strategic Level. Techniques for Human Behavior Modeling. Human Factors. Human–Computer Interaction. Conclusion. References. 10 Verifi cation, Validation, and Accreditation (Mikel D. Petty). Motivation. Background Defi nitions. VV&A Defi nitions. V&V as Comparisons. Performing VV&A. V&V Methods. VV&A Case Studies. Conclusion. Acknowledgments. References. 11 An Introduction to Distributed Simulation (Gabriel A. Wainer and Khaldoon Al-Zoubi). Trends and Challenges of Distributed Simulation. A Brief History of Distributed Simulation. Synchronization Algorithms for Parallel and Distributed Simulation. Distributed Simulation Middleware. Conclusion. References. 12 Interoperability and Composability (Andreas Tolk). Defining Interoperability and Composability. Current Interoperability Standard Solutions. Engineering Methods Supporting Interoperation and Composition. Conclusion. References. Further Readings. Index.

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Book SynopsisAn accessible introduction to the essential quantitative methods for making valuable business decisions Quantitative methods-research techniques used to analyze quantitative data-enable professionals to organize and understand numbers and, in turn, to make good decisions. Quantitative Methods: An Introduction for Business Management presents the application of quantitative mathematical modeling to decision making in a business management context and emphasizes not only the role of data in drawing conclusions, but also the pitfalls of undiscerning reliance of software packages that implement standard statistical procedures. With hands-on applications and explanations that are accessible to readers at various levels, the book successfully outlines the necessary tools to make smart and successful business decisions. Progressing from beginner to more advanced material at an easy-to-follow pace, the author utilizes motivating examples throughout to aid readers interested iTable of ContentsPreface. Part I. Motivations and Foundations. 1 Quantitative Methods: Should we Bother?. 1.1 A decision problem without uncertainty: product mix. 1.2 The role of uncertainty. 1.3 Endogenous vs. exogenous uncertainty: Are we alone?. 1.4 Quantitative models and methods. 1.5 Quantitative analysis and problem solving. Problems. For further reading. References. 2 Calculus. 2.1 A motivating example: economic order quantity. 2.2 A little background. 2.3 Functions. 2.4 Continuous functions. 2.5 Composite functions. 2.6 Inverse functions. 2.7 Derivatives. 2.8 Rules for calculating derivatives. 2.9 Using derivatives for graphing functions. 2.10 Higher-order derivatives and Taylor expansions. 2.11 Convexity and optimization. 2.12 Sequences and series. Problems. For further reading. References. 3 Linear Algebra. 3.1 A motivating example: binomial option pricing. 3.2 Solving systems of linear equations. 3.3 Vector algebra. 3.4 Matrix algebra. 3.5 Linear spaces. 3.6 Determinant. 3.7 Eigenvalues and eigenvectors. 3.8 Quadratic forms. 3.9 Calculus in multiple dimensions. Problems. For further reading. References. Part II Elementary Probability and Statistics. 4 Descriptive Statistics: On the Way to Elementary Probability. 4.1 What is Statistics?. 4.2 Organizing and representing raw data. 4.3 Summary measures. 4.4 Cumulative frequencies and percentiles. 4.5 Multidimensional data. Problems. For further reading. References. 5 Probability Theories. 5.1 Different concepts of probability. 5.2 The axiomatic approach. 5.3 Conditional probability and independence. 5.4 Total probability and Bayes’ theorems. Problems. For further reading. References. 6 Discrete Random Variables. 6.1 Random variables. 6.2 Characterizing discrete distributions. 6.3 Expected value. 6.4 Variance and standard deviation. Problems. For further reading. References. 7 Continuous Random Variables. 7.1 Building intuition: from discrete to continuous random variables. 7.2 Cumulative distribution and probability density functions. 7.3 Expected value and variance. 7.4 Mode, median, and quantiles. 7.5 Higher-order moments, skewness, and kurtosis. 7.6 A few useful continuous probability distributions. 7.7 Sums of independent random variables. 7.8 Miscellaneous applications. 7.9 Stochastic processes. 7.10 Probability spaces, measurability, and information. Problems. For further reading. References. 8 Dependence, Correlation, and Conditional Expectation. 8.1 Joint and marginal distributions. 8.2 Independent random variables. 8.3 Covariance and correlation. 8.4 Jointly normal variables. 8.5 Conditional expectation. Problems. For further reading. References. 9 Inferential Statistics. 9.1 Random samples and sample statistics. 9.2 Confidence intervals. 9.3 Hypothesis testing. 9.4 Beyond the mean of one population. 9.5 Checking the fit of hypothetical distributions: the chi-square test. 9.6 Analysis of variance. 9.7 Monte Carlo simulation. 9.8 Stochastic convergence and the law of large numbers. 9.9 Parameter estimation. 9.10 Some more hypothesis testing theory. Problems. For further reading. References. 10 Simple Linear Regression. 10.1 Least squares method. 10.2 The need for a statistical framework. 10.3 The case of a non-stochastic regressor. 10.4 Using regression models. 10.5 A glimpse of stochastic regressors and heteroskedastic errors. 10.6 A vector space look at linear regression. Problems. For further reading. References. 11 Time Series Models. 11.1 Before we start: Framing the forecasting process. 11.2 Measuring forecasting errors. 11.3 Time series decomposition. 11.4 Moving average. 11.5 Heuristic exponential smoothing. 11.6 A glance at advanced time series modeling. Problems. For further reading. References. Part III Models for Decision Making. 12 Deterministic Decision Models. 12.1 A taxonomy of optimization models. 12.2 Building linear programming models. 12.3 A repertoire of model formulation tricks. 12.4 Building integer programming models. 12.5 Nonlinear programming concepts. 12.6 A glance at solution methods. Problems. For further reading. References. 13 Decision Making under Risk. 13.1 Decision trees. 13.2 Risk aversion and risk measures. 13.3 Two-stage stochastic programming models. 13.4 Multi-stage stochastic linear programming with recourse. 13.5 Robustness, regret, and disappointment. Problems. For further reading. References. 14 Multiple Decision Makers, Subjective Probability, and Other Wild Beasts. 14.1 What is uncertainty?. 14.2 Decision problems with multiple decision makers. 14.3 Incentive misalignment in supply chain management. 14.4 Game theory. 14.5 Braess' paradox for traffic networks. 14.6 Dynamic feedback effects and herding behavior. 14.7 Subjective probability: the Bayesian view. Problems. For further reading. References. Part IV Advanced Statistical Modeling. 15 Introduction to Multivariate Analysis. 15.1 Issues in multivariate analysis. 15.2 An overview of multivariate methods. 15.3 Matrix algebra and multivariate analysis. For further reading. References. 16 Advanced Regression Models. 16.1 Multiple linear regression by least squares. 16.2 Building, testing, and using multiple linear regression models. 16.3 Logistic regression. 16.4 A glance at nonlinear regression. Problems. For further reading. References. 17 Dealing with Complexity: Data Reduction and Clustering. 17.1 The need for data reduction. 17.2 Principal component analysis (PCA). 17.3 Factor analysis. 17.4 Cluster analysis. For further reading. References. Index.

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Book SynopsisPresenting a concise, well-rounded focus on the statistical concepts and applications that are essential for understanding gathered data in the earth and atmospheric sciences.Table of ContentsPreface v Contributors 1 1 Adaptive Sampling 1 2 Agricultural Surveys 8 3 Allometry 20 4 Atmospheric Statistics 27 5 Capture—Recapture Methods—I 35 6 Capture-Recapture Methods—II 43 7 Cluster Sampling 50 8 Distance Sampling 53 9 Ecological Statistics 59 10 Environmental Statistics 67 11 Estimation of Species Richness and Shared Species Richness 76 12 Geographic Information Systems 112 13 Geostatistics 121 14 Landsat Data Analysis in Crop Area Estimation 130 15 Latin Hypercube Sampling 140 16 Line Intercept Sampling 146 17 Line Transect Sampling 148 18 Methods for Drought Analysis and Forecasting 150 19 Ranked Set Sampling Methodology for Environmental Data 185 20 Spatial Data Analysis 212 21 Spatial Processes 216 22 Spatial Sampling 223 23 Statistics in Agriculture 228 24 Statistics in Animal Science 232 25 Statistics in Biogeography 239 26 Statistics in Fisheries Research 246 27 Statistics in Forestry 251 28 Statistics in Geography 266 29 Statistics in Geology 272 30 Statistics in Geophysics 276 31 Statistics in Ornithology 292 32 Stereology 304 33 Stylometry 310 34 Systematic Sampling 318 35 Traffic Flow Problems 322 36 Transect Methods 326 37 Weather Modification—I 331 38 Weather Modification—II 338 39 Wildlife Sampling 349 Index 354

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Book SynopsisPraise for the First Edition . . . an excellent textbook . . . an indispensable reference for biostatisticians and epidemiologists. International Statistical Institute A new edition of the definitive guide to classical and modern methods of biostatistics Biostatistics consists of various quantitative techniques that are essential to the description and evaluation of relationships among biologic and medical phenomena. Biostatistical Methods: The Assessment of Relative Risks, Second Edition develops basic concepts and derives an expanded array of biostatistical methods through the application of both classical statistical tools and more modern likelihood-based theories. With its fluid and balanced presentation, the book guides readers through the important statistical methods for the assessment of absolute and relative risks in epidemiologic studies and clinical trials with categorical, count, and event-time data. Presenting a brTrade Review"Biostatistical methods, second edition is an excellent book for biostatistics courses at the graduate level. It is also an invaluable reference for biostatisticians, applied statisticians, and epidemiologists." (Mathematical Reviews, 2011) "The author of this book has made a tremendous effort in covering a gamut of tests, methods, and ideas for biostatistical problem solving . . . In conclusion, the book is recommended to all in biostatistics as a technical reference." (Journal of Biopharmaceutical Statistics, 1 September 2012) "...Biostatistics is set apart from other statistics specialties by its focus on the assessment of risks and relative risks through clinical research," states Lachin (George Washington U.) in the preface to the first edition (2001). He developed this graduate text to support a course he launched as a joint initiative of the university's department of statistics, its Biostatistics Center, and the School of Public Health and Health Services. Coverage includes discussion of biostatistics and biomedical science, relative risk estimates and tests for independent groups, sample size, stratified adjusted analysis, case-control and matched studies, applications of maximum likelihood and efficient scores, among other topics." (Book News Inc., February 2011) Table of ContentsPreface. Preface to First Edition. 1 Biostatistics and Biomedical Science. 1.1 Statistics and the Scientific Method. 1.2 Biostatistics. 1.3 Natural History of Disease Progression. 1.4 Types of Biomedical Studies. 1.5 Studies of Diabetic Nephropathy. 2 Relative Risk Estimates and Tests for Independent Groups. 2.1 Probability As a Measure of Risk. 2.2 Measures of Relative Risk. 2.3 Large Sample Distribution. 2.4 Sampling Models Likelihoods. 2.5 Exact Inference. 2.6 Large Sample Inferences. 2.7 SAS PROC FREQ. 2.8 Other Measures of Differential Risk. 2.9 Polychotomous and Ordinal Data. 2.10 Two Independent Groups With Polychotomous Response. 2.11 Multiple Independent Groups. 2.12 Problems. 3 Sample Size, Power, and Efficiency. 3.1 Estimation Precision. 3.2 Power of Z-Tests. 3.3 Test for Two Proportions. 3.4 Power of Chi-Square Tests. 3.5 SAS PROC POWER. 3.6 Efficiency. 3.7 Problems. 4 Stratified-Adjusted Analysis for Independent Groups. 4.1 Introduction. 4.2 Mantel-Haenszel Test and Cochran’s Test. 4.3 Stratified-Adjusted Estimators. 4.4 Nature of Covariate Adjustment. 4.5 Multivariate Tests of Hypotheses. 4.6 Tests of Homogeneity. 4.7 Efficient Tests of No Partial Association. 4.8 Asymptotic Relative Efficiency of Competing Tests. 4.9 Maximin-Efficient Robust Tests. 4.10 Random Effects Model. 4.11 Power and Sample Size for Tests of Association. 4.12 Polychotomous and Ordinal Data. 4.13 Problems. 5 Case-Control and Matched Studies. 5.1 Unmatched Case-Control (Retrospective) Sampling. 5.2 Matching. 5.3 Tests of Association for Matched Pairs. 5.4 Measures of Association for Matched Pairs. 5.5 Pair-Matched Retrospective Study. 5.6 Power Function of McNemar’s Test. 5.7 Stratified Analysis of Pair-Matched Tables. 5.8 Multiple Matching-Mantel-Haenszel Analysis. 5.9 Matched Polychotomous Data. 5.10 Kappa Index of Agreement. 5.11 Problems. 6 Applications of Maximum Likelihood and Efficient Scores. 6.1 Binomial. 6.2 2x2 Table: Product Binomial (Unconditionally). 6.3 2x2 Table, Conditionally. 6.4 Score-Based Estimate. 6.5 Stratified Score Analysis of Independent 2x2 Tables. 6.6 Matched Pairs. 6.7 Iterative Maximum Likelihood. 6.8 Problems. 7 Logistic Regression Models. 7.1 Unconditional Logistic Regression Model. 7.2 Interpretation of the Logistic Regression Model. 7.3 Tests of Significance. 7.4 Interactions. 7.5 Measures of the Strength of Association. 7.6 Conditional Logistic Regression Model for Matched Sets. 7.7 Models for Polychotomous or Ordinal Data. 7.8 Random Effects and Mixed Models. 7.9 Models for Multivariate or Repeated Measures. 7.10 Problems. 8 Analysis of Count Data. 8.1 Event Rates and the Homogeneous Poisson Model. 8.2 Over Dispersed Poisson Model. 8.3 Poisson Regression Model. 8.4 Over Dispersed and Robust Poisson Regression. 8.5 Conditional Poisson Regression for Matched Sets. 8.6 Negative Binomial Models. 8.7 Power and Sample Size. 8.8 Multiple Outcomes. 8.9 Problems. 9 Analysis of Event-Time Data. 9.1 Introduction to Survival Analysis. 9.2 Lifetable Construction. 9.3 Family of Weighted Mantel-Haenszel Tests. 9.4 Proportional Hazards Models. 9.5 Evaluation of Sample Size and Power. 9.6 Additional Models. 9.7 Analysis of Recurrent Events. 9.8 Problems. Appendix Statistical Theory. A.1 Introduction. A.2 Central Limit Theorem and the Law of Large Numbers. A.3 Delta Method. A.4 Slutsky’s Convergence Theorem. A.5 Least Squares Estimation. A.6 Maximum Likelihood Estimation and Efficient Scores. A.7 Tests of Significance. A.8 Explained Variation. A.9 Robust Inference. A.10 Generalized Linear Models and Quasi-Likelihood. A.11 Generalized Estimating Equations (GEE). References. Author Index. Subject Index.

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Book SynopsisA vivid, hands-on discussion of the statistical methods in imaging, optics, and photonics applications In the field of imaging science, there is a growing need for students and practitioners to be equipped with the necessary knowledge and tools to carry out quantitative analysis of data. Providing a self-contained approach that is not too heavily statistical in nature, Statistics for Imaging, Optics, and Photonics presents necessary analytical techniques in the context of real examples from various areas within the field, including remote sensing, color science, printing, and astronomy. Bridging the gap between imaging, optics, photonics, and statistical data analysis, the author uniquely concentrates on statistical inference, providing a wide range of relevant methods. Brief introductions to key probabilistic terms are provided at the beginning of the book in order to present the notation used, followed by discussions on multivariate techniques such as: Trade Review“In a word, this is a well-structured volume which will meet the demand visualised by the author.” (Contemporary Physics, 6 December 2013) "The monograph is applicable for courses on multivariate statistics for imaging science, optics, and photonics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for professionals working in imaging, optics, and photonics who carry out data analyses in their everyday work." (Zentralblatt MATH, 1 August 2013) Table of ContentsPreface xiii 1 Introduction 1 1.1 Who Should Read This Book 6 1.2 How This Book is Organized 6 1.3 How to Read This Book and Learn from It 7 1.4 Note for Instructors 8 1.5 Book Web Site 9 2 Fundamentals of Statistics 11 2.1 Statistical Thinking 11 2.2 Data Format 13 2.3 Descriptive Statistics 14 2.3.1 Measures of Location 14 2.3.2 Measures of Variability 16 2.4 Data Visualization 17 2.4.1 Dot Plots 17 2.4.2 Histograms 19 2.4.3 Box Plots 23 2.4.4 Scatter Plots 24 2.5 Probability and Probability Distributions 26 2.5.1 Probability and Its Properties 26 2.5.2 Probability Distributions 30 2.5.3 Expected Value and Moments 33 2.5.4 Joint Distributions and Independence 34 2.5.5 Covariance and Correlation 38 2.6 Rules of Two and Three Sigma 40 2.7 Sampling Distributions and the Laws of Large Numbers 41 2.8 Skewness and Kurtosis 44 3 Statistical Inference 51 3.1 Introduction 51 3.2 Point Estimation of Parameters 53 3.2.1 Definition and Properties of Estimators 53 3.2.2 The Method of the Moments and Plug-In Principle 56 3.2.3 The Maximum Likelihood Estimation 57 3.3 Interval Estimation 60 3.4 Hypothesis Testing 63 3.5 Samples From Two Populations 71 3.6 Probability Plots and Testing for Population Distributions 73 3.6.1 Probability Plots 74 3.6.2 Kolmogorov–Smirnov Statistic 75 3.6.3 Chi-Squared Test 76 3.6.4 Ryan–Joiner Test for Normality 76 3.7 Outlier Detection 77 3.8 Monte Carlo Simulations 79 3.9 Bootstrap 79 4 Statistical Models 85 4.1 Introduction 85 4.2 Regression Models 85 4.2.1 Simple Linear Regression Model 86 4.2.2 Residual Analysis 94 4.2.3 Multiple Linear Regression and Matrix Notation 96 4.2.4 Geometric Interpretation in an n-Dimensional Space 99 4.2.5 Statistical Inference in Multiple Linear Regression 100 4.2.6 Prediction of the Response and Estimation of the Mean Response 104 4.2.7 More on Checking the Model Assumptions 107 4.2.8 Other Topics in Regression 110 4.3 Experimental Design and Analysis 111 4.3.1 Analysis of Designs with Qualitative Factors 116 4.3.2 Other Topics in Experimental Design 124 Supplement 4A. Vector and Matrix Algebra 125 Vectors 125 Matrices 127 Eigenvalues and Eigenvectors of Matrices 130 Spectral Decomposition of Matrices 130 Positive Definite Matrices 131 A Square Root Matrix 131 Supplement 4B. Random Vectors and Matrices 132 Sphering 134 5 Fundamentals of Multivariate Statistics 137 5.1 Introduction 137 5.2 The Multivariate Random Sample 139 5.3 Multivariate Data Visualization 143 5.4 The Geometry of the Sample 148 5.4.1 The Geometric Interpretation of the Sample Mean 148 5.4.2 The Geometric Interpretation of the Sample Standard Deviation 149 5.4.3 The Geometric Interpretation of the Sample Correlation Coefficient 150 5.5 The Generalized Variance 151 5.6 Distances in the p-Dimensional Space 159 5.7 The Multivariate Normal (Gaussian) Distribution 163 5.7.1 The Definition and Properties of the Multivariate Normal Distribution 163 5.7.2 Properties of the Mahalanobis Distance 166 6 Multivariate Statistical Inference 173 6.1 Introduction 173 6.2 Inferences About a Mean Vector 173 6.2.1 Testing the Multivariate Population Mean 173 6.2.2 Interval Estimation for the Multivariate Population Mean 175 6.2.3 T 2 Confidence Regions 179 6.3 Comparing Mean Vectors from Two Populations 183 6.3.1 Equal Covariance Matrices 184 6.3.2 Unequal Covariance Matrices and Large Samples 185 6.3.3 Unequal Covariance Matrices and Samples Sizes Not So Large 186 6.4 Inferences About a Variance–Covariance Matrix 187 6.5 How to Check Multivariate Normality 188 7 Principal Component Analysis 193 7.1 Introduction 193 7.2 Definition and Properties of Principal Components 195 7.2.1 Definition of Principal Components 195 7.2.2 Finding Principal Components 196 7.2.3 Interpretation of Principal Component Loadings 200 7.2.4 Scaling of Variables 207 7.3 Stopping Rules for Principal Component Analysis 209 7.3.1 Fair-Share Stopping Rules 210 7.3.2 Large-Gap Stopping Rules 213 7.4 Principal Component Scores 217 7.5 Residual Analysis 220 7.6 Statistical Inference in Principal Component Analysis 227 7.6.1 Independent and Identically Distributed Observations 227 7.6.2 Imaging Related Sampling Schemes 228 7.7 Further Reading 238 8 Canonical Correlation Analysis 241 8.1 Introduction 241 8.2 Mathematical Formulation 242 8.3 Practical Application 245 8.4 Calculating Variability Explained by Canonical Variables 246 8.5 Canonical Correlation Regression 251 8.6 Further Reading 256 Supplement 8A. Cross-Validation 256 9 Discrimination and Classification – Supervised Learning 261 9.1 Introduction 261 9.2 Classification for Two Populations 264 9.2.1 Classification Rules for Multivariate Normal Distributions 267 9.2.2 Cross-Validation of Classification Rules 277 9.2.3 Fisher’s Discriminant Function 280 9.3 Classification for Several Populations 284 9.3.1 Gaussian Rules 284 9.3.2 Fisher’s Method 286 9.4 Spatial Smoothing for Classification 291 9.5 Further Reading 293 10 Clustering – Unsupervised Learning 297 10.1 Introduction 297 10.2 Similarity and Dissimilarity Measures 298 10.2.1 Similarity and Dissimilarity Measures for Observations 298 10.2.2 Similarity and Dissimilarity Measures for Variables and Other Objects 304 10.3 Hierarchical Clustering Methods 304 10.3.1 Single Linkage Algorithm 305 10.3.2 Complete Linkage Algorithm 312 10.3.3 Average Linkage Algorithm 315 10.3.4 Ward Method 319 10.4 Nonhierarchical Clustering Methods 320 10.4.1 K-Means Method 320 10.5 Clustering Variables 323 10.6 Further Reading 325 Appendix A Probability Distributions 329 Appendix B Data Sets 349 Appendix C Miscellanea 355 References 365 Index 371

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Book SynopsisThis book addresses the statistical, practical, and ethical issues arising from allocating groups of individuals, or clusters, to different interventions.Trade Review“There are several unique strengths to this book. In particular the authors are very experienced statisticians having worked for many years in the design and analysis of cluster randomized trials and have written excellent methodological articles many of which are cited in their book.” (Journal of Biopharmaceutical Statistics, 2012) Table of ContentsPreface xiii Notation xv Table of cases: Trials used as examples in more than one chapter in the book xviii 1 Introduction 1 1.1 Introduction to randomised trials 2 1.2 Explanatory or pragmatic trials 2 1.3 How does a cluster randomised trial differ from other trials? 3 1.4 Between-cluster variability 9 1.5 Why carry out cluster randomised trials? 10 1.6 Quality of evidence from cluster randomised trials 13 1.7 Historical perspectives 16 1.8 Summary 18 References 19 2 Recruitment and ethics 22 2.1 Selecting clusters and participants to enhance external validity 22 2.2 Ethics of cluster randomised trials 24 2.3 Selection and recruitment of participants to enhance internal validity 35 2.4 Retention of participants in the trial 41 2.5 Summary 41 References 41 3 Designing interventions 44 3.1 Lack of effectiveness of interventions evaluated in cluster randomised trials 45 3.2 What is a complex intervention? 46 3.3 Phases in the development of a complex intervention 50 3.4 Identifying evidence for potential intervention effect (pre-clinical phase) 50 3.5 Understanding more about intervention components (modelling phase) 53 3.6 Developing the optimum intervention and study design (exploratory trial phase) 55 3.7 What is the intervention? 57 3.8 Summary 58 References 58 4 Pilot and feasibility studies 60 4.1 What is a pilot study? 60 4.2 Reasons for conducting pilot and feasibility studies 63 4.3 Designing a pilot or feasibility study 69 4.4 Reporting and interpreting pilot studies 71 4.5 Summary 72 References 73 5 Design 74 5.1 Parallel designs with only two arms 75 5.2 Cohort versus cross-sectional designs 85 5.3 Parallel designs with more than two arms 88 5.4 Crossover designs 92 5.5 Further design considerations 95 5.6 Summary 96 References 96 6 Analysis 99 6.1 Data collection and management 99 6.2 Analysis – an introduction 101 6.3 Analyses for two-arm, completely randomised, stratified or minimised designs 104 6.4 Analyses for other designs 124 6.5 Intention to treat and missing values 129 6.6 Analysis planning 131 6.7 Summary 132 References 133 7 Sample size calculations 137 7.1 Factors affecting sample size for cluster randomised designs 138 7.2 Calculating sample size using the intra-cluster correlation coeffi cient 142 7.3 Sample size calculations for rates 145 7.4 Restricted number of clusters 146 7.5 Trials with a small number of clusters 149 7.6 Variability in cluster size 150 7.7 Comparison of different measures of between-cluster variability 154 7.8 Matched and stratifi ed designs 160 7.9 Sample size for other designs 166 7.10 Summary 169 References 169 8 The intra-cluster correlation coeffi cient 172 8.1 What is the ICC? 173 8.2 Sources of ICC estimates 175 8.3 Choosing the ICC for use in sample size calculations 179 8.4 Calculating ICC values 185 8.5 Uncertainty in ICCs 192 8.6 Summary 193 References 193 9 Other topics 196 Richard Grieve 9.1 Systematic reviews 197 9.2 Cost effectiveness analyses 207 9.3 Process evaluation 212 9.4 Monitoring 213 9.5 Summary 215 References 215 10 Trial reporting 218 10.1 Trial quality and reporting quality 218 10.2 Steps to improve trial reporting in the early stages of the trial 227 10.3 Reporting randomised trials in journal and conference abstracts 230 10.4 Application of CONSORT statement to cluster randomised trials 232 10.5 Summary 262 References 263 Index 267

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Book SynopsisThis book provides coverage of topics currently dispersed throughout data mining and business books, bringing them together for the first time to provide readers with an introductory and practical guide to the mathematical models and analysis methodologies vital to business intelligence.Table of ContentsPreface. I. COMPONENTS OF THE DECISION MAKING PROCESS. 1. Business intelligence. 1.1 Effective and timely decisions. 1.2 Data, information and knowledge. 1.3 The role of mathematical models. 1.4 Business intelligence architectures. 1.5 Ethics and business intelligence. 1.6 Notes and readings. 2. Decision support systems. 2.1 Definition of system. 2.2 Representation of the decision making process. 2.3 Evolution of information. 2.4 Definition of decision support system. 2.5 Development of a decision support system. 2.6 Notes and readings. 3. Data warehousing. 3.1 Definition of data warehouse. 3.2 Data warehouse architecture. 3.3 Cubes and multidimensional analysis. 3.4 Notes and readings. II. MATHEMATICAL MODELS AND METHODS. 4. Mathematical models for decision making. 4.1 Structure of mathematical models. 4.2 Development of a model. 4.3 Classes of models. 4.4 Notes and readings. 5. Data mining. 5.1 Definition of data mining. 5.2 Representation of input data. 5.3 Data mining process. 5.4 Analysis methodologies. 5.5 Notes and readings. 6. Data preparation. 6.1 Data validation. 6.2 Data transformation. 6.3 Data reduction. 7. Data exploration. 7.1 Univariate analysis. 7.2 Bivariate analysis. 7.3 Multivariate analysis. 7.4 Notes and readings. 8. Regression. 8.1 Structure of regression models. 8.2 Simple linear regression. 8.3 Multiple linear regression. 8.4 Validation of regression models. 8.5 Selection of predictive variables. 8.6 Notes and readings. 9. Time series. 9.1 Definition of time series. 9.2 Evaluating time series models. 9.3 Analysis of the components of time series. 9.4 Exponential smoothing models. 9.5 Autoregressive models. 9.6 Combination of predictive models. 9.7 The forecasting process. 9.8 Notes and readings. 10. Classification. 10.1 Classification problems. 10.2 Evaluation of classification models. 10.3 Classification trees. 10.4 Bayesian methods. 10.5 Logistic regression. 10.6 Neural networks. 10.7 Support vector machines. 10.8 Notes and readings. 11. Association rules. 11.1 Motivation and structure of association rules. 11.2 Single-dimension association rules. 11.3 Apriori algorithm. 11.4 General association rules. 11.5 Notes and readings. 12. Clustering. 12.1 Clustering methods. 12.2 Partition methods. 12.3 Hierarchical methods. 12.4 Evaluation of clustering models. 12.5 Notes and readings. III. BUSINESS INTELLIGENCE APPLICATIONS. 13. Marketing models. 13.1 Relational marketing. 13.2 Salesforce management. 13.3 Business cases. 13.4 Notes and readings. 14. Logistic and production models. 14.1 Supply chain optimization. 14.2 Optimization models for logistics planning. 14.3 Revenue management systems. 14.4 Business cases. 14.5 Notes and readings. 15. Data envelopment analysis. 15.1 Efficiency measures. 15.2 Efficient frontier. 15.3 The CCR model. 15.4 Identification of good operating practices. 15.5 Other models. 15.6 Notes and readings. A Software tools. B Dataset repositories. References. Index.

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Book SynopsisEveryday we face decisions that carry an element of risk and uncertainty. The ability to analyze, predict, and prepare for the level of risk entailed by these decisions is, therefore, one of the most constant and vital skills needed for analysts, scientists and managers. Risk analysis can be defined as a systematic use of information to identify hazards, threats and opportunities, as well as their causes and consequences, and then express risk. In order to successfully develop such a systematic use of information, those analyzing the risk need to understand the fundamental concepts of risk analysis and be proficient in a variety of methods and techniques. Risk Analysis adopts a practical, predictive approach and guides the reader through a number of applications. Risk Analysis: Provides an accessible and concise guide to performing risk analysis in a wide variety of fields, with minimal prior knowledge required. Adopts a broad peTrade Review"This book provides accessible and concise guidance to the process of planning, execution and use of risk analysis in the context of risk management, with minimal prior knowledge required."(Journal of Applied Statistics, August 2009) "The author has been successful in presenting the subject of risk analysis effectively. The reviewer would like to recommend this book to all those who would like to know about risk analysis and how it can be performed in practical situations." (International Journal of Performability Engineering, January 2009) Table of ContentsPreface. Part I: Theory and methods. 1 What is a risk analysis? 1.1 Why risk analysis?. 1.2 Risk management. 1.2.1 Decision-making under uncertainty. 1.3 Examples: decision situations. 1.3.1 Risk analysis for a tunnel. 1.3.2 Risk analysis for an offshore installation. 1.3.3 Risk analysis related to a cash depot. 2 What is risk? 2.1 Vulnerability. 2.2 How to describe risk quantitatively. 2.2.1 Description of risk in a financial context. 2.2.2 Description of risk in a safety context. 3 The risk analysis process: planning. 3.1 Problem definition. 3.2 Selection of analysis method. 3.2.1 Checklist-based approach. 3.2.2 Risk-based approach. 4 The risk analysis process: risk assessment. 4.1 Identification of initiating events. 4.2 Cause analysis. 4.3 Consequence analysis. 4.4 Probabilities and uncertainties. 4.5 Risk picture: Risk presentation. 4.5.1 Sensitivity and robustness analyses. 4.5.2 Risk evaluation. 5 The risk analysis process: risk treatment. 5.1 Comparisons of alternatives. 5.1.1 How to assess measures? 5.2 Management review and judgement. 6 Risk analysis methods. 6.1 Coarse risk analysis. 6.2 Job safety analysis. 6.3 Failure modes and effects analysis. 6.3.1 Strengths and weaknesses of an FMEA. 6.4 Hazard and operability studies. 6.5 SWIFT. 6.6 Fault tree analysis. 6.6.1 Qualitative analysis. 6.6.2 Quantitative analysis. 6.7 Event tree analysis. 6.7.1 Barrier block diagrams. 6.8 Bayesian networks. 6.9 Monte Carlo simulation. Part II Examples of applications. 7 Safety measures for a road tunnel. 7.1 Planning. 7.1.1 Problem definition. 7.1.2 Selection of analysis method. 7.2 Risk assessment. 7.2.1 Identification of initiating events. 7.2.2 Cause analysis. 7.2.3 Consequence analysis. 7.2.4 Risk picture. 7.3 Risk treatment. 7.3.1 Comparison of alternatives. 7.3.2 Management review and decision. 8 Risk analysis process for an offshore installation. 8.1 Planning. 8.1.1 Problem definition. 8.1.2 Selection of analysis method. 8.2 Risk analysis. 8.2.1 Hazard identification. 8.2.2 Cause analysis. 8.2.3 Consequence analysis. 8.3 Risk picture and comparison of alternatives. 8.4 Management review and judgement. 9 Production assurance. 9.1 Planning. 9.2 Risk analysis. 9.2.1 Identification of failures. 9.2.2 Cause analysis. 9.2.3 Consequence analysis. 9.3 Risk picture and comparison of alternatives. 9.4 Management review and judgement. Decision. 10 Risk analysis process for a cash depot. 10.1 Planning. 10.1.1 Problem definition. 10.1.2 Selection of analysis method. 10.2 Risk analysis. 10.2.1 Identification of hazards and threats. 10.2.2 Cause analysis. 10.2.3 Consequence analysis. 10.3 Risk picture. 10.4 Risk-reducing measures. 10.4.1 Relocation of the NOKAS facility. 10.4.2 Erection of a wall. 10.5 Management review and judgment. Decision. 10.6 Discussion. 11 Risk analysis process for municipalities. 11.1 Planning . 11.1.1 Problem definition. 11.1.2 Selection of analysis method. 11.2 Risk assessment. 11.2.1 Hazard and threat identification. 11.2.2 Cause and consequence analysis. Risk picture. 11.3 Risk treatment. 12 Risk analysis process for the entire enterprise. 12.1 Planning. 12.1.1 Problem definition. 12.1.2 Selection of analysis method. 12.2 Risk analysis. 12.2.1 Price risk. 12.2.2 Operational risk. 12.2.3 Health, Environment and Safety (HES). 12.2.4 Reputation risk. 12.3 Overall risk picture. 12.4 Risk treatment. 13 Discussion. 13.1 Risk analysis as a decision support tool. 13.2 Risk is more than the calculated probabilities and expected values. 13.3 Risk analysis has both strengths and weaknesses. 13.3.1 Precision of a risk analysis: uncertainty and sensitivity analysis. 13.3.2 Terminology. 13.3.3 Risk acceptance criteria (tolerability limits). 13.4 Reflection on approaches, methods and results. 13.5 Limitations of the causal chain approach. 13.6 Risk perspectives. 13.7 Scientific basis. 13.8 The implications of the limitations of risk assessment. 13.9 Critical systems and activities. 13.10 Conclusions. A Probability calculus and statistics. A.1 The meaning of a probability. A.2 Probability calculus. A.3 Probability distributions: expected value. A.3.1 Binomial distribution. A.4 Statistics (Bayesian statistics). B Introduction to reliability analysis. B.1 Reliability of systems composed of components. B.2 Production system. B.3 Safety system. C Approach for selecting risk analysis methods. C.1 Expected consequences. C.2 Uncertainty factors. C.3 Frame conditions. C.4 Selection of a specific method. D Terminology. D.1 Risk management: relationships between key terms. Bibliography. Index.

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Book SynopsisThis book provides an introduction to both Bayesian methods and gene expression, accessible to people with backgrounds in either. The text is enhanced by the inclusion of numerous problems and solutions, designed with an emphasis on methodology and application.Trade Review“The target audience for this book is clearly statisticians rather than biologists … It does provide a very useful overview of statistical genomics for anyone working in the field.” (The Quarterly Review of Biology, 1 March 2012) "Bioinformatics researchers from many fields will find much value in this book." (Mathematical Reviews, 2011) "Experienced readers will find the review of advanced methods for bioinformatics challenging and attainable. This book will interest graduate students in statistics and bioinformatics researchers from many fields." (Book News, December 2009)Table of ContentsTable of Notation. 1 Bioinformatics and Gene Expression Experiments. 1.1 Introduction. 1.2 About This Book. 2 Basic Biology. 2.1 Background. 2.1.1 DNA Structures and Transcription. 2.2 Gene Expression Microarray Experiments. 3 Bayesian Linear Models for Gene Expression. 3.1 Introduction. 3.2 Bayesian Analysis of a Linear Model. 3.3 Bayesian Linear Models for Differential Expression. 3.4 Bayesian ANOVA for Gene Selection. 3.5 Robust ANOVA model with Mixtures of Singular Distributions. 3.6 Case Study. 3.7 Accounting for Nuisance Effects. 3.8 Summary and Further Reading. 4 Bayesian Multiple Testing and False Discovery Rate Analysis. 4.1 Introduction to Multiple Testing. 4.2 False Discovery Rate Analysis. 4.3 Bayesian False Discovery Rate Analysis. 4.4 Bayesian Estimation of FDR. 4.5 FDR and Decision Theory. 4.6 FDR and bFDR Summary. 5 Bayesian Classification for Microarray Data. 5.1 Introduction. 5.2 Classification and Discriminant Rules. 5.3 Bayesian Discriminant Analysis. 5.4 Bayesian Regression Based Approaches to Classification. 5.5 Bayesian Nonlinear Classification. 5.6 Prediction and Model Choice. 5.7 Examples. 5.8 Discussion. 6 Bayesian Hypothesis Inference for Gene Classes. 6.1 Interpreting Microarray Results. 6.2 Gene Classes. 6.3 Bayesian Enrichment Analysis. 6.4 Multivariate Gene Class Detection. 6.5 Summary. 7 Unsupervised Classification and Bayesian Clustering. 7.1 Introduction to Bayesian Clustering for Gene Expression Data. 7.2 Hierarchical Clustering. 7.3 K-Means Clustering. 7.4 Model-Based Clustering. 7.5 Model-Based Agglomerative Hierarchical Clustering. 7.6 Bayesian Clustering. 7.7 Principal Components. 7.8 Mixture Modeling. 7.8.1 Label Switching. 7.9 Clustering Using Dirichlet Process Prior. 7.9.1 Infinite Mixture of Gaussian Distributions. 8 Bayesian Graphical Models. 8.1 Introduction. 8.2 Probabilistic Graphical Models. 8.3 Bayesian Networks. 8.4 Inference for Network Models. 9 Advanced Topics. 9.1 Introduction. 9.2 Analysis of Time Course Gene Expression Data. 9.3 Survival Prediction Using Gene Expression Data. Appendix A: Basics of Bayesian Modeling. A.1 Basics. A.1.1 The General Representation Theorem. A.1.2 Bayes’ Theorem. A.1.3 Models Based on Partial Exchangeability. A.1.4 Modeling with Predictors. A.1.5 Prior Distributions. A.1.6 Decision Theory and Posterior and Predictive Inferences. A.1.7 Predictive Distributions. A.1.8 Examples. A.2 Bayesian Model Choice. A.3 Hierarchical Modeling. A.4 Bayesian Mixture Modeling. A.5 Bayesian Model Averaging. Appendix B: Bayesian Computation Tools. B.1 Overview. B.2 Large-Sample Posterior Approximations. B.2.1 The Bayesian Central Limit Theorem. B.2.2 Laplace’s Method. B.3 Monte Carlo Integration. B.4 Importance Sampling. B.5 Rejection Sampling. B.6 Gibbs Sampling. B.7 The Metropolis Algorithm and Metropolis–Hastings. B.8 Advanced Computational Methods. B.8.1 Block MCMC. B.8.2 Truncated Posterior Spaces. B.8.3 Latent Variables and the Auto-Probit Model. B.8.4 Bayesian Simultaneous Credible Envelopes. B.8.5 Proposal Updating. B.9 Posterior Convergence Diagnostics. B.10 MCMC Convergence and the Proposal. B.10.1 Graphical Checks for MCMC Methods. B.10.2 Convergence Statistics. B.10.3 MCMC in High-Throughput Analysis. B.11 Summary. References. Index.

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Book SynopsisIt is said if you take care of the pennies, the pounds will take care of themselves. Richard Burton''s excellent book takes this approach to calculations applied to the biomedical sciencesThis is certainly interesting and engaging but it avoids being complicated. Journal of Biological Education, April 2009 Biomedical Calculations: Principles and Practice is an accessible, student-friendly introduction to calculating, applying formulae and solving quantitative problems within these subjects. This book targets a problem area for many students and aims to give them the confidence which they are so often lacking when undertaking scientific calculations. It takes a unique approach to the subject and uses unit analysis as a central theme throughout the book to enhance student understanding. Clearly structured throughout, little basic knowledge of mathematics is assumed, but even the most numerate readers will be interested in the sometimes-novel biological detTrade Review?I really liked this book and the approach the author has taken. It will certainly be useful for biomedical scientists and for those teaching potential biomedical scientists, either at university or during training in the laboratory.? ( British J of Biomedical Science, 2008) "It is said if you take care of the pennies, the pounds will take care of themselves. Richard Burton's excellent book takes this approach to calculations applied to the biomedical sciences?This is certainly interesting and engaging but it avoids being complicated." ?Journal of Biological Education, April 2009Table of ContentsPreface. Acknowledgements. This book, and how to use it. PART I. 1. Unit analysis: the neglected key to confidence. 1.1 Calculating with units. 1.2 Ways of writing composite units. 1.3 How unit analysis can guide thinking and help solve problems. 1.4 When to specify substances along with units. 1.5 The need to use appropriate and compatible units in formulae. 1.6 Checking and deriving formulae. 1.7 When unit analysis raises questions about formulae. 1.8 Dimensional analysis. PART II. 2. Units: length, area, volume, mass, moles and equivalents. 2.1 The Système International and unit prefixes. 2.2 Length and distance. 2.3 Area. 2.4 Volume. 2.5 Mass. 2.6 Moles. 2.7 Equivalents. 2.8 Conversion between units. Problems. 3. Percentages. 3.1 When percentages mislead: human body fat and fat in milk. 3.2 Heat loss from the body: further questionable percentages. Problems. 4. Composite units I - density. 4.1 Specific gravity. 4.2 Specific volume. 4.3 Two definitions of body density. 4.4 Thinking about a formula. Problems. 5. Composite units II - concentration. 5.1 Concentrations: kilograms of water vs litres of solution. 5.2 Simple protein-free salt solutions. 5.3 Millimolar and millimolal concentrations in blood plasma. 5.4 Some quite different uses for Eq. (5.1). Problems. 6. Aspects of problem solving. 6.1 Letting unit analysis solve the problem. 6.2 ‘Let x be the unknown’. Problems. 7. Making up and diluting solutions. 7.1 Preparing 250 mL of 150 mM NaCl from the dry salt. 7.2 Preparing dilutions from stock solutions. Problems. 8. Calculating drug doses. Problems. 9. More about solutions - electroneutrality, osmotic pressure and activity. 9.1 The principle of electroneutrality. 9.2 But what about membrane potentials and short-circuit currents? 9.3 Anion gap. 9.4 Osmoles and osmolality. 9.5 Osmolar gap. 9.6 Osmosity. 9.7 Cell contents. 9.8 Effective osmolality, effective osmotic pressure. 9.9 Osmotic shifts of water between cells and extracellular fluid. 9.10 Free and bound concentrations, activities. PART III. 10. Graphs, straight lines and equations. 10.1 Graphs: some terminology. 10.2 Advice on drawing graphs. 10.3 The equation of a straight line. 10.4 Finding the equation of a line that passes through two specified points. 10.5 Drawing a line that is defined by a specified equation. 10.6 Finding the equation of a line from its gradient and the coordinates of a single point on it. 10.7 Finding the line that best fits a number of points when these lie only roughly in a straight line. 10.8 ‘Proportional’ and ‘inversely proportional’. 10.9 Gradients of curves. 10.10 A note on units. 10.11 On the different kinds of formulae and equations. Problems. 11. On shapes and sizes. 11.1 Areas and volumes of simple shapes. 11.2 Erythrocytes, cylinders and spheres. 11.3 The swelling of erythrocytes in hypo-osmotic solutions. 11.4 Distortion of erythrocytes in passing along narrow blood vessels. 11.5 An exercise in rearranging equations to eliminate an unwanted term. 11.6 Easy and general ways to check algebraic working. 11.7 Solving the equation by trial and error in a spreadsheet. 11.8 Why do we not have naturally spherical erythrocytes? 11.9 General properties of simple geometrical shapes. 11.10 Replacing volumes with masses in these equations. 11.11 A digression on graphs. 11.12 Calculating surface area from volume and height: another exercise in re-arranging equations and eliminating unwanted terms. 11.13 Another digression to check algebraic working. 11.14 Generalizing the formula to include the human body. 11.15 Surface/volume and surface/mass ratios. 11.16 The surface area of the human body. 11.17 Standard formulae for body surface area. 11.18 An exercise in comparing formulae containing exponents. Problems. 12. Body size, body build, fatness and muscularity: unit analysis as an aid to discovery. 12.1 Variations in fat-free mass with height and age. 12.2 The Rohrer index, or ‘height-weight index of build’. 12.3 The body mass index; estimating body fat from body mass and height. 12.4 Upper arm muscle: how its cross-sectional area varies with body height. 12.5 Weightlifting - and the cross-sectional area of muscle. 12.6 Estimating body fat from skinfold thickness measurements. 12.7 Postscript. PART IV. 13. Introducing time. 13.1 Frequency. 13.2 Speed and velocity. 13.3 Acceleration. 13.4 Rates of flow of substances carried in fluids. 13.5 Thinking about a formula. 13.6 The concept of renal clearance. 13.7 Relating the clearance formula for renal plasma flow to the Fick Principle. 13.8 Creatinine clearance as a measure of GFR, and a convenient formula for estimating it. Problems. PART V. 14. Force, pressure, energy, work and power. 14.1 Force and weight. 14.2 Pressure. 14.3 Columns of water, columns of blood. 14.4 Osmotic pressure and colloid osmotic pressure (oncotic pressure). 14.5 Energy and work. 14.6 Power. 14.7 An overview of units - from mass to pressure and power. Problems. 15. Lessons from another formula. 15.1 Poiseuille’s equation and viscosity. 15.2 Peripheral resistance. Problems. 16. Heat and temperature. 16.1 Temperature scales. 16.2 The temperature coefficient, Q10. 16.3 Heat capacity and specific heat. Problems. 17. Gases: dry and wet gas mixtures, partial pressures, gases in solution. 17.1 A reminder of units. 17.2 Natural variations in atmospheric pressure. 17.3 The gas laws. 17.4 A closer look at Eq. (17.1) and the universal gas constant, with attention to units. 17.5 Treatment of gas mixtures - percentages. 17.6 Treatment of gas mixtures - partial pressures, tensions. 17.7 Water vapour pressure. 17.8 ‘Standard temperature and pressure, dry’. 17.9 Dissolved O2 and CO2 in blood plasma and other fluids. Problems. PART VI. 18. Introduction to logarithms. 18.1 Definitions. 18.2 Rules for working with logarithms. 18.3 The usefulness of remembering log102. 18.4 Logarithmic scales on graphs. 18.5 What about units? 18.6 Natural logarithms. Problems. 19. Exponential time courses. 19.1 Use of semi-logarithmic plots. 19.2 Common complications. Problems. 20. Nernst equations in physiology and biochemistry: logarithms and ‘RT/zF’. 20.1 More on RT/zF. Problems. 21. pH - two definitions and a possible dilemma for teachers. 21.1 pH as -log[H+]. 21.2 The true definition of pH: pH as a number on a conventional scale. 21.3 The meaning of 10-pH. 21.4 Final comments. Problems. 22. Equilibrium constants, the Henderson-Hasselbalch equation, dose-response curves. 22.1 Equilibrium constants. 22.2 Concentrations or activities? 22.3 The Henderson-Hasselbalch equation. 22.4 Application of the Henderson-Hasselbalch equation to drugs. 22.5 The dependence of [AB] on [A] when ([B] + [AB]) is constant. 22.6 Concentration-response curves, dose-response curves. Problems. 23. Buffering and acid-base balance. 23.1 Non-bicarbonate buffering. 23.2 A link with dose-response curves. 23.3 Bicarbonate buffering. 23.4 CO2/HCO3- and non-bicarbonate buffers together. 23.5 The whole body: diet and the titratable acidity of urine. 23.6 Other aspects of acid-base balance. Problems. References. Appendix A. Basic mathematics and mathematical language. Appendix B. Some non-metric units. Appendix C. Notes. Appendix D. Solutions to problems. Index.

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Book SynopsisThis book provides a modern, hands-on guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics. It acknowledges the limitations of established techniques and provides concrete applications of newly developed methods.Trade Review“Overall the book is an excellent reference for biostatisticians who are interested in methodological approaches as well as for biostatisticians who prefer the applied side. Several useful examples from clinical trials and health research are carefully selected and analyzed to demonstrate the methods covered in the book. It is also a useful resource for postgraduate students researching missing-data methods and their application.” (Biometrical Journal, 1 June 2015) Table of Contents1 Missing Data Concepts and Motivating Examples 1 2 Overview of Methods for Dealing with Missing Data 15 3 Design Considerations in the Presence Of Missing Data 25 4 Cross-sectional Data Methods 31 5 Longitudinal Data Methods 69 6 Survival Analysis Under Ignorable Missingness 121 7 Nonignorable Missingness 147 8 Analysis of Randomized Clinical Trials With Noncompliance 185 Bibliography 215 Index 225

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Book SynopsisMathematical Methods in Biology uniquely covers both deterministic and probabilistic models, including algorithms in the MATLAB platform. The book focuses mostly in one area of the life sciences, focusing mainly on theoretical ecology.Trade Review"Admirably, the volume is written with bits of MATLAB code inserted at appropriate places and has exercises interspersed throughout the text (as well as hints for solutions to the exercises at the end of the book)." The Quarterly Review of Biology, June 2010) "The mathematical and reasoning sophistication increases as the chapters proceed." (Book News, December 2009)Table of ContentsPreface. 1. Introduction To Ecological Modeling. 1.1 Mathematical Models. 1.2 Rates of Change. 1.3 Balance Laws. 1.4 Temperature in the Environment. 1.5 Dimensionless Variables. 1.6 Descriptive Statistics. 1.7 Regression and Curve Fitting. 1.8 Reference Notes. 2. Population Dynamics for Single Species. 2.1 Laws of Population Dynamics. 2.2 Continuous Time Models. 2.3 Qualitative Analysis of Population Models. 2.4 Dynamics of Predation. 2.5 Discrete Time Models. 2.6 Equilibria, Stability, and Chaos. 2.7 Reference Notes. 3. Structure and Interacting Populations. 3.1 Structure--Juveniles and Adults. 3.2 Structured Linear Models. 3.3 Nonlinear Interactions. 3.4 Appendix--Matrices. 3.5 Reference Notes. 4. Interactions in Continuous Time. 4.1 Interacting Populations. 4.2 Phase Plane Analysis. 4.3 Linear Systems. 4.4 Nonlinear Systems. 4.5 Bifurcation. 4.6 Reference Notes. 5. Concepts of Probability. 5.1 Introductory Examples and Definitions. 5.2 The Hardy-Weinberg Law. 5.3 Continuous Random Variables. 5.4 Discrete Random Variables. 5.5 Joint Probability Distributions. 5.6 Covariance and Correlation. 5.7 Reference Notes. 6. Statistical Inference. 6.1 Introduction. 6.2 Interval Analysis. 6.3 Estimating Proportions. 6.4 The Chi-Squared Test. 6.5 Hypothesis Testing. 6.6 Bootstrap Methods. 6.7 Reference Notes. 7. Stochastic Processes. 7.1 Introduction. 7.2 Randomizing Discrete Dynamics. 7.3 Random Walk. 7.4 Birth Processes. 7.5 Stochastic Differential Equations. 7.6 SDEs from Markov Models. 7.7 Solving SDEs. 7.8 The Fokker-Planck Equation. 7.9 Reference Notes. A. Hints and Solutions to Exercises

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Book SynopsisThis book addresses the many important problems in service operations management, which can be analyzed using two core methodologies: optimization and queueing theory (including numerical simulation of queues).Trade Review"The book is well written and very easy to follow. The reviewer highly recommends the book to be considered as a textbook for courses on service operations at the senior-undergraduate and graduate levels." (A Journal for the Worldwide Service Science Community, 2011) Table of ContentsPreface. Acknowledgements. 1. Why study services? 1.1 What are services. 1.2 Services as a percent of the economy. 1.3 Public versus private service delivery. 1.4 Why model services? 1.5 Key service decisions. 1.6 Philosophy about models. 1.7 Outline of the book. 1.8 Problems. 1.9 References. METHODOLOGICAL FOUNDATIONS. 2 Optimization. 2.1 Introduction. 2.2 Five key elements of optimization. 2.3 Taxonomy of optimization models. 2.4 You probably have seen one already. 2.5 Linear programming. 2.6 Special network form. 2.7 Integer problems. 2.8 Multiple objective problems. 2.9 Mark’s ten rules of formulating problems. 2.10 Problems. 2.11 References. 3 Queueing theory. 3.1 Introduction. 3.2 What is a queueing theory? 3.3 Key performance metrics for queues and Little’s formula. 3.4 A framework for Markovian queues. 3.5 Key results for non-Markovian queues. 3.6 Solving queueing models numerically. 3.7 When conditions change over time. 3.8 Conclusions. 3.9 Problems. 3.10 References. APPLICATION AREAS. 4 Location and districting problems in services. 4.1 Example applications. 4.2 Taxonomy of location problems. 4.3 Covering problems. 4.4 Median problems - minimizing the demand-weighted average distance. 4.5 Multi-objective models. 4.6 Districting problems. 4.7 Franchise location problems. 4.8 Summary and software. 4.9 Problems. 4.10 References. 5 Inventory decisions in services. 5.1 Why is inventory in a service modeling book? 5.2 EOQ - a basic inventory model. 5.3 Extensions of the EOQ model. 5.4 Time varying demand. 5.5 Uncertain demand and lead times. 5.6 Newsvendor problem and applications. 5.7 Summary. 5.8 Problems. 5.9 References. 6 Resource allocation problems and decisions in services. 6.1 Example resource allocation problems. 6.2 How to formulate an assignment or resource allocation problem. 6.3 Infeasible solutions. 6.4 Assigning students to freshman seminars. 6.5 Assigning students to intersession courses. 6.6 Improving the assignment of zip codes to Congressional districts. 6.7 Summary. 6.8 Problems. 6.9 References. 7 Short-term workforce scheduling. 7.1 Overview of scheduling. 7.2 Simple model. 7.3 Extensions of the simple model. 7.4 More difficult extensions. 7.5 Linking scheduling to service. 7.6 Time-dependent queueing analyzer. 7.7 Assigning specific employees to shifts. 7.8 Summary. 7.9 Problems. 7.10 References. 8 Long-term workforce planning. 8.1 Why is long-term workforce planning an issue? 8.2 Basic model. 8.3 Grouping of skills. 8.4 Planning over time. 8.5 Linking to project scheduling. 8.6 Linking to personnel training and planning in general. 8.7 Simple model of training. 8.8 Summary. 8.9 Problems. 8.10 References. 9 Priority services, call center design and customer scheduling. 9.1 Examples. 9.2 Priority queueing for emergency and other services. service in each class with non-preemptive priorities. 9.2.3 Priority service with Poisson arrivals, multiple servers and identically distributed exponential service times.. 9.2.4 Preemptive queueing. 9.3 Call center design. 9.4 Scheduling in services. 9.5 Summary. 9.6 Problems. 9.7 References. 10 Vehicle routing and services. 10.1 Example routing problems. 10.2 Classification of routing problems. 10.3 Arc routing. 10.4 The traveling salesman problem. 10.5 Vehicle routing problems. 10.6 Summary. 10.7 Problems. 10.8 References. 11 Where to from here? 11.1 Introduction. 11.2 Other methodologies. 11.3 Other applications in services. 11.4 Summary. 11.5 References. APPENDICES. A. Sums of series - basic formulae. B. Overview of probability. B.1. Introduction and basic definitions. B.2 Axioms of probability .. B.3 Joint, marginal and conditional probabilities and Bayes’ theorem. B.4 Counting, ordered pairs, permutations and combinations. B.5 Random variables. B.6 Discrete random variables. B.7 Continuous random variables. B.8 Moment and probability generating functions. B.9 Generating random variables. B.10 Random variables in Excel. C. References.

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Book SynopsisThis new, modernized edition provides a clear and thorough introduction to matrix computations,a key component of scientific computing Retaining the accessible and hands-on style of its predecessor, Fundamentals of Matrix Computations, Third Edition thoroughly details matrix computations and the accompanying theory alongside the author''s useful insights. The book presents the most important algorithms of numerical linear algebra and helps readers to understand how the algorithms are developed and why they work. Along with new and updated examples, the Third Edition features: A novel approach to Francis'' QR algorithm that explains its properties without reference to the basic QR algorithm Application of classical Gram-Schmidt with reorthogonalization A revised approach to the derivation of the Golub-Reinsch SVD algorithm New coverage on solving product eigenvalue problems Expanded treatment of the Jacobi-Table of ContentsPreface. Acknowledgments. 1 Gaussian Elimination and Its Variants. 1.1 Matrix Multiplication. 1.2 Systems of Linear Equations. 1.3 Triangular Systems. 1.4 Positive Definite Systems; Cholesky Decomposition. 1.5 Banded Positive Definite Systems. 1.6 Sparse Positive Definite Systems. 1.7 Gaussian Elimination and the LU Decomposition. 1.8 Gaussain Elimination and Pivoting. 1.9 Sparse Gaussian Elimination. 2 Sensitivity of Linear Systems. 2.1 Vector and Matrix Norms. 2.2 Condition Numbers. 2.3 Perturbing the Coefficient Matrix. 2.4 A Posteriori Error Analysis Using the Residual. 2.5 Roundoff Errors; Backward Stability. 2.6 Propagation of Roundoff Errors. 2.7 Backward Error Analysis of Gaussian Elimination. 2.8 Scaling. 2.9 Componentwise Sensitivity Analysis. 3 The Least Squares Problem. 3.1 The Discrete Square Problem. 3.2 Orthogonal Matrices, Rotators and Reflectors. 3.3 Solution of the Least Squares Problem. 3.4 The Gram-Schmidt Process. 3.5 Geometric Approach. 3.6 Updating the QR Decomposition. 4 The Singular Value Decomposition. 4.1 Introduction. 4.2 Some Basic Applications of Singular Values. 4.3 The SVD and the Least Squares Problem. 4.4 Sensitivity of the Least Squares Problem. 5 Eigenvalues and Eigenvectors I. 5.1 Systems of Differential Equations. 5.2 Basic Facts. 5.3 The Power Method and Some Simple Extensions. 5.4 Similarity Transforms. 5.5 Reduction to Hessenberg and Tridiagonal Forms. 5.6 Francis's Algorithm. 5.7 Use of Francis's Algorithm to Calculate Eigenvectors. 5.8 The SVD Revisted. 6 Eigenvalues and Eigenvectors II. 6.1 Eigenspaces and Invariant Subspaces. 6.2 Subspace Iteration and Simultaneous Iteration. 6.3 Krylov Subspaces and Francis's Algorithm. 6.4 Large Sparse Eigenvalue Problems. 6.5 Implicit Restarts. 6.6 The Jacobi-Davidson and Related Algorithms. 7 Eigenvalues and Eigenvectors III. 7.1 Sensitivity of Eigenvalues and Eigenvectors. 7.2 Methods for the Symmetric Eigenvalue Problem. 7.3 Product Eigenvalue Problems. 7.4 The Generalized Eigenvalue Problem. 8 Iterative Methods for Linear Systems. 8.1 A Model Problem. 8.2 The Classical Iterative Methods. 8.3 Convergence of Iterative Methods. 8.4 Descent Methods; Steepest Descent. 8.5 On Stopping Criteria. 8.6 Preconditioners. 8.7 The Conjugate-Gradient Method. 8.8 Derivation of the CG Algorithm. 8.9 Convergence of the CG Algorithm. 8.10 Indefinite and Nonsymmetric Problems. References. Index. Index of MATLAB Terms.

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Book SynopsisThis book teaches statistics with a modern, data-analytic approach that uses graphing calculators and statistical software. It allows more emphasis to be put on statistical concepts and data analysis than on following recipes for calculations. This gives readers a more realistic understanding of both the theoretical and practical applications of statistics, giving them the ability to master the subject.

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Book SynopsisAn accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction andmotivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysisand variance reduction; and applications ranging from option pricing and risk management to optimization. The Handbook in Monte Carlo Simulation features: An introductorTable of ContentsPreface xiii Part I Overview and Motivation 1 Introduction to Monte Carlo Methods 3 1.1 Historical origin of Monte Carlo simulation 4 1.2 Monte Carlo Simulation vs. Monte Carlo Sampling 7 1.3 System dynamics and the mechanics of Monte Carlo simulation 10 1.4 Simulation and optimization 21 1.5 Pitfalls in Monte Carlo simulation 30 1.6 Software tools for Monte Carlo simulation 35 1.7 Prerequisites 37 For further reading 38 Chapter References 38 2 Numerical Integration Methods 41 2.1 Classical quadrature formulae 43 2.2 Gaussian quadrature 48 2.3 Extension to higher dimensions: Product rules 53 2.4 Alternative approaches for high-dimensional integration 55 2.5 Relationship with moment matching 67 2.6 Numerical integration in R 69 For further reading 71 Chapter References 71 Part II Input Analysis: Modeling and Estimation 3 Stochastic Modeling in Finance and Economics 75 3.1 Introductory examples 77 3.2 Some common probability distributions 86 3.3 Multivariate distributions: Covariance and correlation 111 3.4 Modeling dependence with copulae 127 3.5 Linear regression models: a probabilistic view 136 3.6 Time series models 137 3.7 Stochastic differential equations 158 3.8 Dimensionality reduction 177 S3.1 Risk-neutral derivative pricing 190 S3.1.1 Option pricing in the binomial model 192 S3.1.2 A continuous-time model for option pricing: The Black–Scholes–Merton formula 194 S3.1.3 Option pricing in incomplete markets 199 For further reading 202 Chapter References 203 4 Estimation and Fitting 205 4.1 Basic inferential statistics in R 207 4.2 Parameter estimation 215 4.3 Checking the fit of hypothetical distributions 224 4.4 Estimation of linear regression models by ordinary least squares 229 4.5 Fitting time series models 232 4.6 Subjective probability: the Bayesian view 235 For further reading 244 Chapter References 245 Part III Sampling and Path Generation 5 Random Variate Generation 249 5.1 The structure of a Monte Carlo simulation 250 5.2 Generating pseudo-random numbers 252 5.3 The inverse transform method 263 5.4 The acceptance–rejection method 265 5.5 Generating normal variates 269 5.6 Other ad hoc methods 274 5.7 Sampling from copulae 276 For further reading 277 Chapter References 279 6 Sample Path Generation for Continuous-Time Models 281 6.1 Issues in path generation 282 6.2 Simulating geometric Brownian motion 287 6.3 Sample paths of short-term interest rates 298 6.4 Dealing with stochastic volatility 306 6.5 Dealing with jumps 308 For further reading 310 Chapter References 311 Part IV Output Analysis and Efficiency Improvement 7 Output Analysis 315 7.1 Pitfalls in output analysis 317 7.2 Setting the number of replications 323 7.3 A world beyond averages 325 7.4 Good and bad news 327 For further reading 327 Chapter References 328 8 Variance Reduction Methods 329 8.1 Antithetic sampling 330 8.2 Common random numbers 336 8.3 Control variates 337 8.4 Conditional Monte Carlo 341 8.5 Stratified sampling 344 8.6 Importance sampling 350 For further reading 363 Chapter References 363 9 Low-Discrepancy Sequences 365 9.1 Low-discrepancy sequences 366 9.2 Halton sequences 367 9.3 Sobol low-discrepancy sequences 374 9.4 Randomized and scrambled low-discrepancy sequences 379 9.5 Sample path generation with low-discrepancy sequences 381 For further reading 385 Chapter References 385 Part V Miscellaneous Applications 10 Optimization 389 10.1 Classification of optimization problems 390 10.2 Optimization model building 405 10.3 Monte Carlo methods for global optimization 412 10.4 Direct search and simulation-based optimization methods 416 10.5 Stochastic programming models 420 10.6 Scenario generation and Monte Carlo methods for stochastic programming 428 10.7 Stochastic dynamic programming 433 10.8 Numerical dynamic programming 440 10.9 Approximate dynamic programming 451 For further reading 453 Chapter References 453 11 Option Pricing 455 11.1 European-style multidimensional options in the BSM world 456 11.2 European-style path-dependent options in the BSM world 462 11.3 Pricing options with early exercise features 475 11.4 A look outside the BSM world 487 11.5 Pricing interest-rate derivatives 490 For further reading 497 Chapter References 498 12 Sensitivity Estimation 501 12.1 Estimating option greeks by finite differences 503 12.2 Estimating option greeks by pathwise derivatives 509 12.3 Estimating option greeks by the likelihood ratio method 513 For further reading 517 Chapter References 518 13 Risk Measurement and Management 519 13.1 What is a risk measure? 520 13.2 Quantile-based risk measures: value at risk 522 13.3 Monte Carlo methods for V@R 533 13.4 Mean-risk models in stochastic programming 537 13.5 Simulating delta-hedging strategies 540 13.6 The interplay of financial and nonfinancial risks 546 For further reading 548 Chapter References 548 14 Markov Chain Monte Carlo and Bayesian Statistics 551 14.1 An introduction to Markov chains 552 14.2 The Metropolis–Hastings algorithm 555 14.3 A re-examination of simulated annealing 558 For further reading 560 Chapter References 561 Index 563

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Book SynopsisThis new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.Table of ContentsPREFACE xv ACKNOWLEDGMENTS xvii 1 REVIEW 1 1.1 Mathematical Notation 1 1.2 Taylor’s Theorem and Mathematical Limit Theory 2 1.3 Statistical Notation and Probability Distributions 4 1.4 Likelihood Inference 9 1.5 Bayesian Inference 11 1.6 Statistical Limit Theory 13 1.7 Markov Chains 14 1.8 Computing 17 PART I OPTIMIZATION 2 OPTIMIZATION AND SOLVING NONLINEAR EQUATIONS 21 2.1 Univariate Problems 22 2.2 Multivariate Problems 34 Problems 54 3 COMBINATORIAL OPTIMIZATION 59 3.1 Hard Problems and NP-Completeness 59 3.2 Local Search 65 3.3 Simulated Annealing 68 3.4 Genetic Algorithms 75 3.5 Tabu Algorithms 85 Problems 92 4 EM OPTIMIZATION METHODS 97 4.1 Missing Data, Marginalization, and Notation 97 4.2 The EM Algorithm 98 4.3 EM Variants 111 Problems 121 PART II INTEGRATION AND SIMULATION 5 NUMERICAL INTEGRATION 129 5.1 Newton–Côtes Quadrature 129 5.2 Romberg Integration 139 5.3 Gaussian Quadrature 142 5.4 Frequently Encountered Problems 146 Problems 148 6 SIMULATION AND MONTE CARLO INTEGRATION 151 6.1 Introduction to the Monte Carlo Method 151 6.2 Exact Simulation 152 6.3 Approximate Simulation 163 6.4 Variance Reduction Techniques 180 Problems 195 7 MARKOV CHAIN MONTE CARLO 201 7.1 Metropolis–Hastings Algorithm 202 7.2 Gibbs Sampling 209 7.3 Implementation 218 Problems 230 8 ADVANCED TOPICS IN MCMC 237 8.1 Adaptive MCMC 237 8.2 Reversible Jump MCMC 250 8.3 Auxiliary Variable Methods 256 8.4 Other Metropolis–Hastings Algorithms 260 8.5 Perfect Sampling 264 8.6 Markov Chain Maximum Likelihood 268 8.7 Example: MCMC for Markov Random Fields 269 Problems 279 PART III BOOTSTRAPPING 9 BOOTSTRAPPING 287 9.1 The Bootstrap Principle 287 9.2 Basic Methods 288 9.3 Bootstrap Inference 292 9.4 Reducing Monte Carlo Error 302 9.5 Bootstrapping Dependent Data 303 9.6 Bootstrap Performance 315 9.7 Other Uses of the Bootstrap 316 9.8 Permutation Tests 317 Problems 319 PART IV DENSITY ESTIMATION AND SMOOTHING 10 NONPARAMETRIC DENSITY ESTIMATION 325 10.1 Measures of Performance 326 10.2 Kernel Density Estimation 327 10.3 Nonkernel Methods 341 10.4 Multivariate Methods 345 Problems 359 11 BIVARIATE SMOOTHING 363 11.1 Predictor–Response Data 363 11.2 Linear Smoothers 365 11.3 Comparison of Linear Smoothers 377 11.4 Nonlinear Smoothers 379 11.5 Confidence Bands 384 11.6 General Bivariate Data 388 Problems 389 12 MULTIVARIATE SMOOTHING 393 12.1 Predictor–Response Data 393 12.2 General Multivariate Data 413 Problems 416 DATA ACKNOWLEDGMENTS 421 REFERENCES 423 INDEX 457

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Book Synopsis* This textbook has been in constant use since 1980, and this edition has been rewritten to be even cleaner and clearer and new features have been introduced. * The authors continue to provide real-world, technical applications that promote intuitive reader learning.Table of Contents1 Review of Numerical Computation 1 1–1 The Real Numbers 2 1–2 Addition and Subtraction 9 1–3 Multiplication 15 1–4 Division 19 1–5 Powers and Roots 23 1–6 Combined Operations 29 1–7 Scientific Notation and Engineering Notation 32 1–8 Units of Measurement 41 1–9 Percentage 51 Chapter 1 Review Problems 59 2 Introduction to Algebra 62 2–1 Algebraic Expressions 63 2–2 Adding and Subtracting Polynomials 67 2–3 Laws of Exponents 72 2–4 Multiplying a Monomial by a Monomial 80 2–5 Multiplying a Monomial and a Multinomial 83 2–6 Multiplying a Binomial by a Binomial 86 2–7 Multiplying a Multinomial by a Multinomial 88 2–8 Raising a Multinomial to a Power 90 2–9 Dividing a Monomial by a Monomial 92 2–10 Dividing a Polynomial by a Monomial 95 2–11 Dividing a Polynomial by a Polynomial 98 Chapter 2 Review Problems 101 3 Simple Equations and Word Problems 103 3–1 Solving a Simple Equation 104 3–2 Solving Word Problems 113 3–3 Uniform Motion Applications 118 3–4 Money Problems 121 3–5 Applications Involving Mixtures 123 3–6 Statics Applications 127 3–7 Applications to Work, Fluid Flow, and Energy Flow 129 Chapter 3 Review Problems 133 4 Functions 136 4–1 Functions and Relations 137 4–2 More on Functions 144 Chapter 4 Review Problems 154 5 Graphs 156 5–1 Rectangular Coordinates 157 5–2 Graphing an Equation 161 5–3 Graphing a Function by Calculator 164 5–4 The Straight Line 167 5–5 Solving an Equation Graphically 172 Chapter 5 Review Problems 173 6 Geometry 175 6–1 Straight Lines and Angles 176 6–2 Triangles 180 6–3 Quadrilaterals 187 6–4 The Circle 190 6–5 Polyhedra 196 6–6 Cylinder, Cone, and Sphere 201 Chapter 6 Review Problems 205 7 Right Triangles and Vectors 207 7–1 The Trigonometric Functions 208 7–2 Solution of Right Triangles 212 7–3 Applications of the Right Triangle 216 7–4 Angles in Standard Position 221 7–5 Introduction to Vectors 222 7–6 Applications of Vectors 226 Chapter 7 Review Problems 229 8 Oblique Triangles and Vectors 231 8–1 Trigonometric Functions of Any Angle 232 8–2 Finding the Angle When the Trigonometric Function Is Known 236 8–3 Law of Sines 240 8–4 Law of Cosines 246 8–5 Applications 251 8–6 Non-Perpendicular Vectors 255 Chapter 8 Review Problems 260 9 Systems of Linear Equations 263 9–1 Systems of Two Linear Equations 264 9–2 Applications 270 9–3 Other Systems of Equations 279 9–4 Systems of Three Equations 284 Chapter 9 Review Problems 290 10 Matrices and Determinants 292 10–1 Introduction to Matrices 293 10–2 Solving Systems of Equations by the Unit Matrix Method 297 10–3 Second-Order Determinants 302 10–4 Higher-Order Determinants 308 Chapter 10 Review Problems 316 11 Factoring and Fractions 319 11–1 Common Factors 320 11–2 Difference of Two Squares 323 11–3 Factoring Trinomials 326 11–4 Other Factorable Expressions 333 11–5 Simplifying Fractions 335 11–6 Multiplying and Dividing Fractions 340 11–7 Adding and Subtracting Fractions 344 11–8 Complex Fractions 349 11–9 Fractional Equations 352 11–10 Literal Equations and Formulas 355 Chapter 11 Review Problems 360 12 Quadratic Equations 363 12–1 Solving a Quadratic Equation Graphically and by Calculator 364 12–2 Solving a Quadratic by Formula 368 12–3 Applications 372 Chapter 12 Review Problems 377 13 Exponents and Radicals 379 13–1 Integral Exponents 380 13–2 Simplification of Radicals 385 13–3 Operations with Radicals 392 13–4 Radical Equations 398 Chapter 13 Review Problems 403 14 Radian Measure, Arc Length, and Rotation 405 14–1 Radian Measure 406 14–2 Arc Length 413 14–3 Uniform Circular Motion 416 Chapter 14 Review Problems 420 15 Trigonometric, Parametric, and Polar Graphs 422 15–1 Graphing the Sine Wave by Calculator 423 15–2 Manual Graphing of the Sine Wave 430 15–3 The Sine Wave as a Function of Time 435 15–4 Graphs of the Other Trigonometric Functions 441 15–5 Graphing a Parametric Equation 448 15–6 Graphing in Polar Coordinates 452 Chapter 15 Review Problems 459 16 Trigonometric Identities and Equations 461 16–1 Fundamental Identities 462 16–2 Sum or Difference of Two Angles 469 16–3 Functions of Double Angles and Half-Angles 474 16–4 Evaluating a Trigonometric Expression 481 16–5 Solving a Trigonometric Equation 484 Chapter 16 Review Problems 489 17 Ratio, Proportion, and Variation 491 17–1 Ratio and Proportion 492 17–2 Similar Figures 497 17–3 Direct Variation 501 17–4 The Power Function 505 17–5 Inverse Variation 509 17–6 Functions of More Than One Variable 513 Chapter 17 Review Problems 518 18 Exponential and Logarithmic Functions 521 18–1 The Exponential Function 522 18–2 Logarithms 532 18–3 Properties of Logarithms 539 18–4 Solving an Exponential Equation 547 18–5 Solving a Logarithmic Equation 554 Chapter 18 Review Problems 560 19 Complex Numbers 562 19–1 Complex Numbers in Rectangular Form 563 19–2 Complex Numbers in Polar Form 568 19–3 Complex Numbers on the Calculator 572 19–4 Vector Operations Using Complex Numbers 575 19–5 Alternating Current Applications 578 Chapter 19 Review Problems 584 20 Sequences, Series, and the Binomial Theorem 586 20–1 Sequences and Series 587 20–2 Arithmetic and Harmonic Progressions 593 20–3 Geometric Progressions 600 20–4 Infinite Geometric Progressions 604 20–5 The Binomial Theorem 607 Chapter 20 Review Problems 614 21 Introduction to Statistics and Probability 617 21–1 Definitions and Terminology 618 21–2 Frequency Distributions 622 21–3 Numerical Description of Data 628 21–4 Introduction to Probability 638 21–5 The Normal Curve 648 21–6 Standard Errors 654 21–7 Process Control 661 21–8 Regression 669 Chapter 21 Review Problems 674 22 Analytic Geometry 679 22–1 The Straight Line 680 22–2 Equation of a Straight Line 687 22–3 The Circle 694 22–4 The Parabola 702 22–5 The Ellipse 713 22–6 The Hyperbola 725 Chapter 22 Review Problems 733 Appendices A Summary of Facts and Formulas A-0 B Conversion Factors A-0 C Table of Integrals A-0 Indexes Applications Index I-0 Index to Writing Questions I-0 Index to Projects I-0 General Index I-0

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Book SynopsisFrom two authors who embrace technology in the classroom and value the role of collaborative learning comes College Geometry Using The Geometer''s Sketchpad, a book that is ideal for geometry courses for both mathematics and math education majors. The book''s truly discovery-based approach guides students to learn geometry through explorations of topics ranging from triangles and circles to transformational, taxicab, and hyperbolic geometries. In the process, students hone their understanding of geometry and their ability to write rigorous mathematical proofs.Table of ContentsPREFACE xv ONE Using The Geometer’s Sketchpad 1 TWO Constructing → Proving 21 THREE Mathematical Arguments and Triangle Geometry 53 FOUR Circle Geometry and Proofs 85 FIVE Analytic Geometry 111 SIX Taxicab Geometry 143 SEVEN Finite Geometries 161 EIGHT Transformational Geometry 185 NINE Isometries and Matrices 209 TEN Symmetry in the Plane 229 ELEVEN Hyperbolic Geometry 253 TWELVE Projective Geometry 287 APPENDIX A Trigonometry 317 APPENDIX B Calculating with Matrices 329 BIBLIOGRAPHY 337 INDEX 341

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Book SynopsisAs third in the series, this book focuses on a style of data analysis that makes graphics central to exploration. Making Sense of Data III explains how to implement decision support systems and provides an interactive approach to data analysis that allows users to see, manipulate, explore, mine data, and share results with colleagues.Trade Review“It is an essential book for understanding the principal role that graphics play in data visualization.” (Zentralblatt MATH, 1 April 2015) Table of ContentsPreface. 1. Introduction. 1.1 Overview. 1.2 Visual Perception. 1.3 Visualization. 1.4 Designing for High-throughput Data Exploration. 1.5 Summary. 1.6 Further reading. 2. The Cognitive and Visual Systems. 2.1 External Representation. 2.2 The Cognitive System. 2.3 Visual Perception. 2.4 Influencing Visual Perception. 2.5 Summary. 2.6 Further reading. 3. Graphic Representations. 3.1 Jacques Bertin: Semiology of Graphics. 3.2 Wilkinson: Grammar of Graphics. 3.3 Wickham: ggplot2. 3.4 Bostock and Heer: Protovis. 3.5 Summary. 3.6 Further reading. 4. Designing Visual Interactions. 4.1 Designing for Complexity. 4.2 The Process of Design. 4.3 Visual Interaction Design. 5. Hands-on: Creating Interactive Visualizations with Protovis. 5.1 Using Protovis. 5.2 Creating Code using the Protovis Graphical Framework. 5.3 Basic Protovis Marks. 5.4 Creating Customized Plots. 5.5 Creating Basic Plots. 5.6 Data Analysis Graphs. 5.7 Composite Plots. 5.8 Interactive Plots. 5.9 Protovis Summary. 5.10 Further Reading. Appendix. A Exercise Code Examples. Bibliography. Index.

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Book SynopsisA practical, one-stop reference on the theory and applications of statistical data editing and imputation techniques Collected survey data are vulnerable to error. In particular, the data collection stage is a potential source of errors and missing values.Table of ContentsPreface ix 1 Introduction to Statistical Data Editing and Imputation 1 1.1 Introduction 1 1.2 Statistical Data Editing and Imputation in the Statistical Process 4 1.3 Data, Errors, Missing Data, and Edits 6 1.4 Basic Methods for Statistical Data Editing and Imputation 13 1.5 An Edit and Imputation Strategy 17 References 21 2 Methods for Deductive Correction 23 2.1 Introduction 23 2.2 Theory and Applications 24 2.3 Examples 27 2.4 Summary 55 References 55 3 Automatic Editing of Continuous Data 57 3.1 Introduction 57 3.2 Automatic Error Localization of Random Errors 59 3.3 Aspects of the Fellegi–Holt Paradigm 63 3.4 Algorithms Based on the Fellegi–Holt Paradigm 65 3.5 Summary 101 3.A Appendix: Chernikova’s Algorithm 103 References 104 4 Automatic Editing: Extensions to Categorical Data 111 4.1 Introduction 111 4.2 The Error Localization Problem for Mixed Data 112 4.3 The Fellegi–Holt Approach 115 4.4 A Branch-and-Bound Algorithm for Automatic Editing of Mixed Data 129 4.5 The Nearest-Neighbor Imputation Methodology 140 References 158 5 Automatic Editing: Extensions to Integer Data 161 5.1 Introduction 161 5.2 An Illustration of the Error Localization Problem for Integer Data 162 5.3 Fourier–Motzkin Elimination in Integer Data 163 5.4 Error Localization in Categorical, Continuous, and Integer Data 172 5.5 A Heuristic Procedure 182 5.6 Computational Results 183 5.7 Discussion 187 References 189 6 Selective Editing 191 6.1 Introduction 191 6.2 Historical Notes 193 6.3 Micro-selection: The Score Function Approach 195 6.4 Selection at the Macro-level 208 6.5 Interactive Editing 212 6.6 Summary and Conclusions 217 References 219 7 Imputation 223 7.1 Introduction 223 7.2 General Issues in Applying Imputation Methods 226 7.3 Regression Imputation 230 7.4 Ratio Imputation 244 7.5 (Group) Mean Imputation 246 7.6 Hot Deck Donor Imputation 249 7.7 A General Imputation Model 255 7.8 Imputation of Longitudinal Data 261 7.9 Approaches to Variance Estimation with Imputed Data 264 7.10 Fractional Imputation 271 References 272 8 Multivariate Imputation 277 8.1 Introduction 277 8.2 Multivariate Imputation Models 280 8.3 Maximum Likelihood Estimation in the Presence of Missing Data 285 8.4 Example: The Public Libraries 295 References 297 9 Imputation Under Edit Constraints 299 9.1 Introduction 299 9.2 Deductive Imputation 301 9.3 The Ratio Hot Deck Method 311 9.4 Imputing from a Dirichlet Distribution 313 9.5 Imputing from a Singular Normal Distribution 318 9.6 An Imputation Approach Based on Fourier–Motzkin Elimination 334 9.7 A Sequential Regression Approach 338 9.8 Calibrated Imputation of Numerical Data Under Linear Edit Restrictions 343 9.9 Calibrated Hot Deck Imputation Subject to Edit Restrictions 349 References 358 10 Adjustment of Imputed Data 361 10.1 Introduction 361 10.2 Adjustment of Numerical Variables 362 10.3 Adjustment of Mixed Continuous and Categorical Data 377 References 389 11 Practical Applications 391 11.1 Introduction 391 11.2 Automatic Editing of Environmental Costs 391 11.3 The EUREDIT Project: An Evaluation Study 400 11.4 Selective Editing in the Dutch Agricultural Census 420 References 426 Index 429

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Book SynopsisMultiple Comparison Procedures offers an up-to-date view of multiple comparison procedures, disproving the belief held by some statisticians that such procedures have no place in data analysis.Table of Contents1. Introduction 1 1. Two Early Multiple Comparison Procedures 2 2. Basic Notions and Philosophy of Multiple Comparisons 5 3. Examples 12 Part I. Procedures Based in Classical Approaches for Fixed-Effects Linear Models with Normal Homoscedastic Independent Errors 17 2. Some Theory of Multiple Comparison Procedures for Fixed-Effects Linear Models 17 2. Single-Step Procedures for Nonhierarchical Families 28 3. Single-Step Procedures for Hierarchical Families 43 4. Step-Down Procedures 53 3. Single-Step Procedures for Pairwise and More General Comparisons among All Treatments 72 1. Scheffé’s S-Procedure 73 2. Tukey’s T-Procedure for Balanced Designs 80 3. Modifications of the T-Procedure for Unbalanced Designs 85 4. Comparisons among Single-Step Procedures 102 5. Additional Topics 107 4. Stepwise Procedures for Pairwise and More General Comparisons among All Treatments 110 1. Step-Down Procedures Based on F-Statistics 111 2. Step-Down Procedures Based in Studentized Range Statistics 114 3. Peritz’s Closed Step-Down Procedure 121 4. Step-Up Procedures 124 5. A Comparison of Single-Step and Stepwise Procedures 128 5. Procedures for Some Nonhierarchical Finite Families of Comparisons 134 1. Orthogonal Comparisons 135 2. Comparisons with a Control 139 3. Comparisons with the “Best” Treatment 150 4. Two Miscellaneous Families 157 6. Designing Experiments for Multiple Comparisons 161 1. Single-Stage Procedures 163 2. Two-Stage Procedures 171 3. Incomplete Block Designs for Comparing Treatments with a Control 174 Part II. Procedures for Other Models and Problems, and Procedures Based on Alternative Approaches 179 7. Procedures for One-Way Layouts with Unequal Variances 181 1. Single-Stage Procedures 182 2. Two-Stage Procedures 194 3. Step-Down Procedures 204 8. Procedures for Mixed Two-Way Layouts and Designs with Random Covariates 207 1. Procedures for One-Way Repeated Measures and Mixed Two-Way Designs 208 2. Procedures for Analysis of Covariance Designs with Random Covariates 219 9. Distribution-Free and Robust Procedures 234 1. Procedures for One-Way Layouts 235 2. Procedures for Randomized Complete Block Designs 250 3. Procedures Based on Other Approaches 267 4. Robust Procedures 271 10. Some Miscellaneous Multiple Comparison Problems 274 1. Multiple Comparison Procedures for Categorical Data 274 2. Multiple Comparisons of Variances 282 3. Graphical Procedures 286 4. Multiple Comparisons of Means under Order Restrictions 290 5. Interactions in Two-Way Layouts 294 6. Partitioning Treatment Means into Groups 303 11. Optimal Procedures Based on Decision-Theoretic, Bayesian, and Other Approaches 310 1. A Decision-Theoretic Approach 311 2. A Neyman-Pearson Type Approach 314 3. A Bayesian Approach 318 4. A Combined Bayesian and Neyman-Pearson Type Approach 334 5. A Г-Minimax Approach 336 Appendixes 341 1. Some General Theory of Multiple Comparison Procedures 343 2. Some Probability Inequalities Useful in Multiple Comparisons 362 3. Some Probability Distributions and Tables Useful in Multiple Comparisons 373 References 417 Author Index 439 Subject Index 445

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Book SynopsisThis work has been thoughtfully designed so that it serves equally well as a reference for the practitioner and as a self-contained textbook for the advanced student. * Rewritten to maintain clarity and brevity while expanding the coverage of previous editions. * Changes to design-related topics include increased discussion of mixed models and random effects, greater emphasis on regression and data screening, and more use of graphs throughout. * Includes both graded and challenging exercises. * Liberal computer discussions now supplemented with SAS and SPSS.Table of ContentsPreface. 1. Data Screening. 1.1 Variables and Their Classification. 1.2 Describing the Data. 1.3 Departures from Assumptions. 1.4 Summary. 2. One-Way Analysis of Variance Design. 2.1 One-Way Analysis of Variance with Fixed Effects. 2.2 One-Way Analysis of Variance with Random Effects. 2.3 Designing an Observational Study or Experiment. 2.4 Checking if the Data Fit the One-Way ANOVA Model. 2.5 What to Do if the Data Do Not Fit the Model. 2.6 Presentation and Interpretation of Results. 2.7 Summary. 3. Estimation and Simultaneous Inference. 3.1 Estimation for Single Population Means. 3.2 Estimation for Linear Combinations of Population Means. 3.3 Simultaneous Statistical Inference. 3.4 Inference for Variance Components. 3.5 Presentation and Interpretation of Results. 3.6 Summary. 4. Hierarchical or Nested Design. 4.1 Example. 4.2 The Model. 4.3 Analysis of Variance Table and F Tests. 4.4 Estimation of Parameters. 4.5 Inferences with Unequal Sample Sizes. 4.6 Checking If the Data Fit the Model. 4.7 What to Do If the Data Don't Fit the Model. 4.8 Designing a Study. 4.9 Summary. 5. Two Crossed Factors: Fixed Effects and Equal Sample Sizes. 5.1 Example. 5.2 The Model. 5.3 Interpretation of Models and Interaction. 5.4 Analysis of Variance and F Tests. 5.5 Estimates of Parameters and Confidence Intervals. 5.6 Designing a Study. 5.7 Presentation and Interpretation of Results. 5.8 Summary. 6 Randomized Complete Block Design. 6.1 Example. 6.2 The Randomized Complete Block Design. 6.3 The Model. 6.4 Analysis of Variance Table and F Tests. 6.5 Estimation of Parameters and Confidence Intervals. 6.6 Checking If the Data Fit the Model. 6.7 What to Do if the Data Don't Fit the Model. 6.8 Designing a Randomized Complete Block Study. 6.9 Model Extensions. 6.10 Summary. 7. Two Crossed Factors: Fixed Effects and Unequal Sample Sizes. 7.1 Example. 7.2 The Model. 7.3 Analysis of Variance and F Tests. 7.4 Estimation of Parameters and Confidence Intervals. 7.5 Checking If the Data Fit the Two-Way Model. 7.6 What To Do If the Data Don't Fit the Model. 7.7 Summary. 8. Crossed Factors: Mixed Models. 8.1 Example. 8.2 The Mixed Model. 8.3 Estimation of Fixed Effects. 8.4 Analysis of Variance. 8.5 Estimation of Variance Components. 8.6 Hypothesis Testing. 8.7 Confidence Intervals for Means and Variance Components. 8.8 Comments on Available Software. 8.9 Extensions of the Mixed Model. 8.10 Summary. 9. Repeated Measures Designs. 9.1 Repeated Measures for a Single Population. 9.2 Repeated Measures with Several Populations. 9.3 Checking if the Data Fit the Repeated Measures Model. 9.4 What to Do if the Data Don't Fit the Model. 9.5 General Comments on Repeated Measures Analyses. 9.6 Summary. 10. Linear Regression: Fixed X Model. 10.1 Example. 10.2 Fitting a Straight Line. 10.3 The Fixed X Model. 10.4 Estimation of Model Parameters and Standard Errors. 10.5 Inferences for Model Parameters: Confidence Intervals. 10.6 Inference for Model Parameters: Hypothesis Testing. 10.7 Checking if the Data Fit the Regression Model. 10.8 What to Do if the Data Don't Fit the Model. 10.9 Practical Issues in Designing a Regression Study. 10.10 Comparison with One-Way ANOVA. 10.11 Summary. 11. Linear Regression: Random X Model and Correlation. 11.1 Example. 11.2 Summarizing the Relationship Between X and Y. 11.3 Inferences for the Regression of Y and X. 11.4 The Bivariate Normal Model. 11.5 Checking if the Data Fit the Random X Regression Model. 11.6 What to Do if the Data Don't Fit the Random X Model. 11.7 Summary. 12. Multiple Regression. 12.1 Example. 12.2 The Sample Regression Plane. 12.3 The Multiple Regression Model. 12.4 Parameters Standard Errors, and Confidence Intervals. 12.5 Hypothesis Testing. 12.6 Checking If the Data Fit the Multiple Regression Model. 12.7 What to Do If the Data Don't Fit the Model. 12.8 Summary. 13. Multiple and Partial Correlation. 13.1 Example. 13.2 The Sample Multiple Correlation Coefficient. 13.3 The Sample Partial Correlation Coefficient. 13.4 The Joint Distribution Model. 13.5 Inferences for the Multiple Correlation Coefficient. 13.6 Inferences for Partial Correlation Coefficients. 13.7 Checking If the Data Fit the Joint Normal Model. 13.8 What to Do If the Data Don't Fit the Model. 13.9 Summary. 14. Miscellaneous Topics in Regression. 14.1 Models with Dummy Variables. 14.2 Models with Interaction Terms. 14.3 Models with Polynomial Terms. 14.4 Variable Selection. 14.5 Summary. 15. Analysis of Covariance. 15.1 Example. 15.2 The ANCOVA Model. 15.3 Estimation of Model Parameters. 15.4 Hypothesis Tests. 15.5 Adjusted Means. 15.6 Checking If the Data Fit the ANCOVA Model. 15.7 What to Do if the Data Don't Fit the Model. 15.8 ANCOVA in Observational Studies. 15.9 What Makes a Good Covariate. 15.10 Measurement Error. 15.11 ANCOVA versus Other Methods of Adjustment. 15.12 Comments on Statistical Software. 15.13 Summary. 16. Summaries, Extensions, and Communication. 16.1 Summaries and Extensions of Models. 16.2 Communication of Statistics in the Context of Research Project. Appendix A. A.1 Expected Values and Parameters. A.2 Linear Combinations of Variables and Their Parameters. A.3 Balanced One-Way ANOVA, Expected Mean Squares. A.4 Balanced One-Way ANOVA, Random Effects. A.5 Balanced Nested Model. A.6 Mixed Model. A.7 Simple Linear Regression—Derivation of Least Squares Estimators. A.8 Derivation of Variance Estimates from Simple Linear Regression. Appendix B. Index.

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Book SynopsisThis book is a comprehensive treatment of correlation/regression techniques and using SPSS for interpretation of findings. Striking a balance between detailed coverage and approachability, this book provides a thorough treatment of the elements of regression and how they can be used with real research problems in program evaluation. The author begins with a basic introduction to evaluation methodology, and its ability to recognize embedded patterns of meaning in research data. Subsequent chapters explore the statistical tools that can be applied by researchers and evaluators irrespective of the design that was used to generate this data. Topics of coverage include: correlation, single predictor regression, multiple correlation, part and partial correlation, detection of extreme scores, multiple regression, regression with continuous predictors, coding of categorical data, regression with categorical predictors, methods for entering predictors in multiple regression, and inteTable of ContentsCHAPTER ONE: INTRODUCTION. Initial Considerations. Book Plan. Real Examples. Using Statistical Programs. The Evaluator’s Journey. CHAPTER TWO: THE ELEMENTS OF EVALUATION. Nature of Evaluation. Evaluation Concerns. Evaluation Standards. Methods used in Evaluation. The Evaluator’s Tools. Evaluation Hurdles. Quantification. Resistance to Quantification. The Nature of Quantification. Qualitative Methods. Specialization. Statistical Issues. Certainty vs. Probability. Statistical Significance. Effect Sizes. Can We Achieve Certainty? Dispelling the Mystique of Statistics. Research Literacy. The Discovery Questions. School Characteristics and Student Learning. Worker Participation. The Impact of Technology on the Classroom. Classroom Observation Data. Discovery Learning. Terms and Concepts. CHAPTER THREE: Using SPSS? General Features. Management Functions. Reading and Importing Data. Sort. Split File. Transform/compute (creating indices). Merge. Analysis Functions. Graphing Functions. CHAPTER FOUR: CORRELATION. The Nature of Correlation. Prediction. Correlation is not Causation. Pearson’s r. Strength and Direction. A Note on the Nature of the Data. Interpreting Pearson’s r. Testing the Statistical Significance of a Correlation. The "Practical Significance" of r: Effect Sizes. An Evaluation Example of Correlation: The Impact of Technology on Teaching and Learning. Influences on Correlation. Restricted Range. Extreme (outlier) Scores. Other Kinds of Correlation. A Research Example of Spearman’s rho Correlation. Non Linear Correlation. "Extending" Correlation to Include Additional Variables. Correlation - Detail for the Curious. Computing Pearson’s r. Assumptions of Correlation. Non-Linear Correlation. Discovery Learning. Terms and Concepts. Practical Application-Correlation. Description of the Data. Evaluation Questions. CHAPTER FIVE: REGRESSION. The Regression Line - Line of "Best Fit". The Regression Formula. Standard Error of Estimate. Confidence Interval. Residuals. Regression Example with Achievement Data. The Results of the Analysis. The Graph of the Results. Standard Error of Estimate. The Confidence Interval. Detail - for the curious. Assumptions of Regression. Fixed vs. Random Effects Modeling. Non-Linear Correlation. Calculating the Standard Error of the Estimate. Discovery Note. Terms and Concepts. Practical Application - Bivariate Regression. CHAPTER SIX: CLEANING THE DATA - DETECTING OUTLIERS. Univariate Extreme Scores. Multivariate Extreme Scores. Distance Statistics. Influence Statistics. Discovery Note. Terms and Concepts. Practical Application - Extreme Scores. CHAPTER SEVEN: MULTIPLE CORRELATION. Introduction. Control Variables. Mediator Variables. Using Multiple Correlation to Control Variables: Partial & Semi-partial correlation. Partial Correlation. Semi-partial (Part) Correlation. Discovery Note. Terms and Concepts. Practical Application - Partial and Semi-Partial Correlation. CHAPTER EIGHT: MULTIPLE REGRESSION. Multiple Regression With Two Predictor Variables. Uses of Multiple Regression. Multiple Regression Outcomes. Omnibus Findings for the Overall Model. Individual Predictors. Additional SPSS® Results. Multiple Regression: How to Enter Predictors. Stepwise Regression and Other Methods. Assumptions of Multiple Regression. Multicollinearity. Cleaning the Database. Multiple Regression with More Than Two Predictor Variables: Research Examples. Predicting the Impact of School Variables on Teaching and Learning: the TAGLIT Data. Omnibus Findings. Results of Individual Predictors. Discovery Notes. Terms and Concepts. Practical Application: Multiple Regression. CHAPTER NINE: CODING - USING MLR WITH CATEGORICAL VARIABLES. Nature of Dummy Variables. One Categorical Variable with Two Groups. Creating Dummy Variables. Creating Subvariables in SPSS®. One Categorical Variable with More Than Two Groups. A Hypothetical Example. An Example from the School Database. Discovery Notes. Detail for the Curious. False Dichotomies. Discovery Note. Terms and Concepts. Practical Application: Dummy Coding. CHAPTER TEN: INTERACTION. Interactions with Continuous Variables. Interaction with Categorical Variables. Discovery Notes. Terms and Concepts. Practical Application - Interaction. CHAPTER ELEVEN: DISCOVERY LEARNING THROUGH CORRELATION AND REGRESSION. Overall Discovery Notes. Findings from the Data. Student Academic Achievement. Workplace Participation. The Impact of Technology on Student Learning. Advanced Statistical Techniques. Hierarchical Linear Regression. Structural Equation Modeling and Path Analysis. Other Regression Procedures. CHAPTER TWELVE: PRACTICAL APPLICATION ANALYSES. Practical Application: Correlation. Practical Application: Bivariate Regression. Practical Application: Extreme Scores. Practical Application: Partial and Semi-Partial Correlation. Practical Application: Multiple Regression. Practical Application: Dummy Coding. Practical Application: Interaction. REFERENCES.

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Book SynopsisA balanced treatment of the theories, methodologies, and design issues involved in clinical trials using statistical methods There has been enormous interest and development in Bayesian adaptive designs, especially for early phases of clinical trials. However, for phase III trials, frequentist methods still play a dominant role through controlling type I and type II errors in the hypothesis testing framework. From practical perspectives, Clinical Trial Design: Bayesian and Frequentist Adaptive Methods provides comprehensive coverage of both Bayesian and frequentist approaches to all phases of clinical trial design. Before underpinning various adaptive methods, the book establishes an overview of the fundamentals of clinical trials as well as a comparison of Bayesian and frequentist statistics. Recognizing that clinical trial design is one of the most important and useful skills in the pharmaceutical industry, this book provides detailed discussions on a Trade Review“In summary, this book is useful for anyone interested in design and analysis of clinical studies. The main strengths of this book are the well-balanced approach between statistical theories and statistical analysis methods of the design issues involved in clinical trials. I would suggest the usage of this book as a Master’s level course in biostatistics.” (Journal of Biopharmaceutical Statistics, 1 May 2013) “The author should be commended; the text is well written and packed with information that belies the book’s trim size." (Drug Information Journal, 30 October 2012) “The book accompanied with software developed at MD Anderson Cancer Center provides an excellent reference for everyone who works in clinical trial field.” (Biometrics, 1 July 2012)Table of ContentsPreface xv 1. Introduction 1 2. Fundamentals of Clinical Trials 13 3. Frequentist versus Bayesian Statistics 29 4. Phase I Trial Design 77 5. Phase II Trial Design 115 6. Phase III Trial Design 159 7. Adaptive Randomization 223 8. Late-Onset Toxicity 253 9. Drug-Combination Trials 271 10. Targeted Therapy Design 297 References 311 Author Index 329 Subject Index 333

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Book SynopsisExplores the development of the ellipse and presents mathematical concepts within a rich, historical context The Ellipse features a unique, narrative approach when presenting the development of this mathematical fixture, revealing its parallels to mankind''s advancement from the Counter-Reformation to the Enlightenment. Incorporating illuminating historical background and examples, the author brings together basic concepts from geometry, algebra, trigonometry, and calculus to uncover the ellipse as the shape of a planet''s orbit around the sun. The book begins with a discussion that tells the story of man''s pursuit of the ellipse, from Aristarchus to Newton''s successful unveiling nearly two millenniums later. The narrative draws insightful similarities between mathematical developments and the advancement of the Greeks, Romans, Medieval Europe, and Renaissance Europe. The author begins each chapter by setting the historical backdrop that is pertinent to the maTrade Review“This is an interesting book, and despite the shortcomings mentioned at the beginning of this review, it’s an absorbing book, borne of a great enthusiasm for mathematics, and I shall continue to dip into it for a long time to come.” (Journal of the British Society for the History of Mathematics, 1 March 2014) "Recommended. Mathematics and history of mathematics collections serving lower-division undergraduates and above". (Choice, 1 November 2010)Table of ContentsPREFACE. CHAPTER 1: INTRODUCTION. CHAPTER 2: THE TRAIL: STARTING OUT. 2.1 A Sticky Matter. 2.2 Numbers. 2.2.1 Integers, Rational Numbers, and Irrational Numbers. 2.2.2 The Size of the Irrational Numbers. 2.2.3 Suitability of Rationals and the Decimal System. 2.2.4 Rational and Irrational Outcomes. CHAPTER 3: THE SPACE: GEOMETRY. 3.1 Euclidean Space, Dimension and Rescaling. 3.1.1 Euclidean Space and Objects. 3.1.2 Euclidean Space in Higher Dimensions. 3.1.3 Unit Measurements and Measures of Objects. 3.1.4 Rescaling, Measurement, and Dimension. 3.1.5 Koch's Snowflake, a Fractal Object. 3.2 Measurements of Various Objects. 3.2.1 Pythagorean Theorem, Length of the Hypotenuse. 3.2.2 Cavalieri's Theorem in Two Dimensions. 3.2.3 Cavalieri's Theorem, Archimedes Weighs In. 3.2.4 Simple Applications of Cavalieri’s Theorem. 3.2.5 The Circle. 3.2.6 Surface Area of the Cone. 3.2.7 Cavalieri's Theorem a Stronger Version in Three Dimensions. 3.2.8 Generalized Pyramids. 3.2.9 The Sphere as a Generalized Pyramid. 3.2.10 The Surface Area and Volume of the Sphere. 3.2.11 Equal-Area Maps, Another Excursion. CHAPTER 4: THE LANGUAGE: ALGEBRA. 4.1 Cartesian Coordinates and Translation of the Axes. 4.1.1 Intersections of Geometric Objects as Solutions to Equations. 4.1.2 Translation of Axis and Object. 4.2 Polynomials. 4.2.1 Lines. 4.2.2 Parabolas and the Quadratic Equation. 4.3 Circles. 4.3.1 Equations for a Circle. 4.3.2 Archimedes and the Value of π. 4.3.3 Tangent Lines to a Circle. 4.4 The Four-Dimensional Sphere. 4.4.1 Pythagorean Theorem in Higher Dimensions. 4.4.2 Measurements in Higher Dimensions and n-Dimensional Cubes. 4.4.3 Cavalieri's Theorem. 4.4.4 Pyramids. 4.4.5 The n-Dimensional Sphere as an n-Dimensional Pyramid 4.4.6 The Three-Dimensional Volume of the Four-Dimensional Sphere's Surface 4.5 Finite Series and Induction. 4.5.1 A Simple Sum. 4.5.2 Induction. 4.5.3 Using Induction as a Solution Method. 4.6 Linear Algebra in Two Dimensions. 4.6.1 Vectors. 4.6.2 The Span of Vectors. 4.6.3 Linear Transformations of the Plane Onto Itself. 4.6.4 The Inverse of a Linear Transformation. 4.6.5 The Determinant. 4.7 The Ellipse. 4.7.1 The Ellipse as a Linear Transformation of a Circle. 4.7.2 The Equation of an Ellipse. 4.7.3 An Excursion into the Foci of an Ellipse. CHAPTER 5: THE UNIVERSAL TOOL: TRIGONOMETRY. 5.1 Trigonometric Functions. 5.1.1 Basic Definitions. 5.1.2 Triangles. 5.1.3 Examples. 5.2 Graphs of the Sine, Cosine, and Tangent Functions. 5.3 Rotations. 5.4 Identities. 5.4.1 Pythagorean Identity. 5.4.2 Negative of an Angle. 5.4.3 Tan(θ) in Terms of Sin(θ) and Cos(θ). 5.4.4 Sines and Cosines of Sums of Angles. 5.4.5 Difference Formulas. 5.4.6 Double-Angle Formulas. 5.4.7 Half-Angle Formulas. 5.5 Lucky 72. 5.6 Ptolemy and Aristarchus. 5.6.1 Construction of Ptolemy's Table. 5.6.2 Remake of Aristarchus. 5.7 Drawing a Pentagon. 5.8 Polar Coordinates. 5.9 The Determinant. CHAPTER 6: THE SLAYER: CALCULUS. 6.1 Studies of Motion and the Fundamental Theorem of Calculus. 6.1.1 Constant Velocity and Two Problems of Motion. 6.1.2 Differential Calculus, Generalizing the First Problem. 6.1.3 Integral Calculus, Generalizing the Second Problem. 6.1.4 Relations Between Differentiation and Integration and the Fundamental Theorem of Calculus. 6.1.5 Integration, Leibniz’ Notation, and the Fundamental Theorem of Calculus. 6.2 More Motion: Going in Circles. 6.3 More Differential Calculus. 6.3.1 Differentiation Rules. 6.3.2 Notation and the Derivative at a Specified Point. 6.3.3 Higher Order Differentiation and Examples. 6.3.4 Differentiation and the Enquirer. 6.4 More Integral Calculus. 6.4.1 The Antiderivative and the Fundamental Theorem of Calculus. 6.4.2 Methods of Integration. 6.5 Potpourri. 6.5.1 Cavalieri's Theorem and the Fundamental Theorem of Calculus. 6.5.2 Volume of the Sphere and Other Objects with Known Cross-Sectional Areas. CHAPTER 7: EIGHT MINUTES THAT CHANGED HISTORY. 7.1 Newton's Laws of Motion. 7.2 Galilean Checkpoint. 7.3 Constants of Motion and Energy. 7.3.1 Energy of a Tossed Object. 7.3.2 Energy of a System Moving in a Single Dimension. 7.4 Kepler and Newton: Aristarchus Redeemed. 7.4.1 Polar Coordinates. 7.4.2 Angular Momentum. 7.4.3 The Ellipse. EPILOGUE. BIBLIOGRAPHY. INDEX.

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Book SynopsisMaster PowerPoint and improve your presentation skills-with one book!It's no longer enough to have slide after slide of text, bullets, and charts. It's not even enough to have good speaking skills if your PowerPoint slides bore your audience.

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Book SynopsisThis Second Edition of Quantitative Reasoning empowers students to use quantitative information to make responsible financial, environmental, and health-related decisions in their daily lives. Students develop their critical thinking skills through numerous examples, explorations, and activities featuring real data. Students use a variety of analysis throughout the text: inductive and deductive reasoning; tabular, symbolic, verbal, and graphical forms of functions and relations; graphs and pictorial representations of data; interpretations of probabilistic data; surveys and statistical studies. Sevilla and Somer''s Quantitative Reasoning, 2nd Editioncomes available with WileyPLUS, a research-based, online environment for effective teaching and learning, which takes the guesswork out of studying by providing them with a clear roadmap: what to do, how to do it, and whether they did it right. WileyPLUS sold separately from text.Table of ContentsAnnotated Contents xi Preface xvii To the Student xxiii To the Instructor xxv Acknowledgments xxvii Section I: Numerical Reasoning Topic 1: Organizing Information Pictorially Using Charts and Graphs 2 Activity 1.1: World Motor Vehicle Production: Bar Graphs and Pie Charts 27 Activity 1.2: Medical Data and Class Data: Graphs with Excel 35 Activity 1.3: SATs and the Super Bowl: Creating and Interpreting Histograms 43 Topic 2: Bivariate Data 52 Activity 2.1: Estimating Dates: Scatterplots 69 Activity 2.2: State Governors’ Salaries and Per Capita Income: More on Scatterplots 75 Topic 3: Graphs of Functions 82 Activity 3.1: Temperature Patterns: Functions and Line Graphs 105 Activity 3.2: Rates of Change and Concavity 111 Topic 4: Multiple Variable Functions 118 Activity 4.1: Blood Alcohol Levels and Credit Cards: Working with More Than Two Variables 133 Topic 5: Proportional, Linear, and Piecewise Linear Functions 140 Activity 5.1: Rates of Change and Linear Functions 159 Topic 6: Modeling with Linear and Exponential Functions 166 Activity 6.1: The Genie’s Offer: Exponential Growth and Linear Growth 185 Activity 6.2: Lines of Best Fit 191 Topic 7: Logarithms and Scientific Notation 198 Activity 7.1: Richter Scale and Logarithms 213 Activity 7.2: Estimations, Scientifc Notation, and Properties of Logarithms 219 Topic 8: Indexes and Ratings 224 Activity 8.1: Measurement Difficulties and Indexes 243 Activity 8.2: Consumer Indexes 249 Topic 9: Personal Finances 256 Activity 9.1: Mortgages 277 Topic 10: Introduction to Problem Solving 284 Activity 10.1: Savings and Loans: Problem Solving and Using Scroll Bars 299 Activity 10.2: Asking and Answering a Research Question 305 Section II: Logical Reasoning Topic 11: Decision Making 308 Activity 11.1: Ranking Cities: Ratings and Decisions 325 Topic 12: Inductive Reasoning 332 Activity 12.1: Analyzing Studies: Inductive Reasoning 345 Topic 13: Deductive Reasoning 354 Activity 13.1: Code-Breaking and Deductive Reasoning 375 Activity 13.2: Compound Statements Used in Reasoning 381 Activity 13.3: Quantified Statements and Deductive Reasoning: Direct and Indirect Reasoning 389 Topic 14: Apportionment 394 Activity 14.1: Methods of Apportionment: Quota Methods 409 Activity 14.2: Apportionment: Divisor Methods 415 Topic 15: More on Problem Solving 422 Activity 15.1: Making a Purchase Decision 433 Section III: Statistical Reasoning Topic 16: Averages and Five-Number Summary 436 Activity 16.1: Visualizing Football Scores: Measures of Center and Spread 457 Topic 17: Standard Deviation, z-Score, and Normal Distributions 462 Activity 17.1: Coins, Presidents, and Justices: Normal Distributions and z-Scores 485 Topic 18: Basics of Probability 492 Activity 18.1: Simulations 511 Activity 18.2: Finding Probabilities 517 Topic 19: Conditional Probability and Tables 522 Activity 19.1: Diagnostic Testing and Conditional Probability 543 Topic 20: Sampling and Surveys 550 Activity 20.1: Sampling and Surveys 569 Topic 21: More on Decision Making 578 Activity 21.1: To Purchase a Warranty or Not: Making a Decision 593 Appendix Excel Commands by Activity 597 Index 603

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Book SynopsisLearn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive.Trade Review“He concludes, "This book collects a number of interesting ideas in optimal learning, allows for connections to be made across disciplines, and is a welcome addition to my bookshelf.” (Informs Journal on Computing, 1 October 2012) Table of ContentsPreface xv Acknowledgments xix 1 The challenges of learning 1 1.1 Learning the best path 2 1.2 Areas of application 4 1.3 Major problem classes 12 1.4 The different types of learning 13 1.5 Learning from different communities 16 1.6 Information collection using decision trees 18 1.6.1 A basic decision tree 18 1.6.2 Decision tree for offline learning 20 1.6.3 Decision tree for online learning 21 1.6.4 Discussion 25 1.7 Website and downloadable software 26 1.8 Goals of this book 26 Problems 28 2 Adaptive learning 31 2.1 The frequentist view 32 2.2 The Bayesian view 33 2.2.1 The updating equations for independent beliefs 34 2.2.2 The expected value of information 36 2.2.3 Updating for correlated normal priors 38 2.2.4 Bayesian updating with an uninformative prior 41 2.3 Updating for non-Gaussian priors 42 2.3.1 The gamma-exponential model 43 2.3.2 The gamma-Poisson model 44 2.3.3 The Pareto-uniform model 45 2.3.4 Models for learning probabilities* 46 2.3.5 Learning an unknown variance* 49 2.4 Monte Carlo simulation 51 2.5 Why does it work?* 54 2.5.1 Derivation of ~_ 54 2.5.2 Derivation of Bayesian updating equations for independent beliefs 55 2.6 Bibliographic notes 57 Problems 57 3 The economics of information 61 3.1 An elementary information problem 61 3.2 The marginal value of information 65 3.3 An information acquisition problem 68 3.4 Bibliographic notes 70 Problems 70 4 Ranking and selection 71 4.1 The model 72 4.2 Measurement policies 75 4.2.1 Deterministic vs. sequential policies 75 4.2.2 Optimal sequential policies 76 4.2.3 Heuristic policies 77 4.3 Evaluating policies 81 4.4 More advanced topics* 83 4.4.1 An alternative representation of the probability space 83 4.4.2 Equivalence of using true means and sample estimates 84 4.5 Bibliographic notes 85 Problems 85 5 The knowledge gradient 89 5.1 The knowledge gradient for independent beliefs 90 5.1.1 Computation 91 5.1.2 Some properties of the knowledge gradient 93 5.1.3 The four distributions of learning 94 5.2 The value of information and the S-curve effect 95 5.3 Knowledge gradient for correlated beliefs 98 5.4 The knowledge gradient for some non-Gaussian distributions 103 5.4.1 The gamma-exponential model 104 5.4.2 The gamma-Poisson model 107 5.4.3 The Pareto-uniform model 108 5.4.4 The beta-Bernoulli model 109 5.4.5 Discussion 111 5.5 Relatives of the knowledge gradient 112 5.5.1 Expected improvement 113 5.5.2 Linear loss* 114 5.6 Other issues 116 5.6.1 Anticipatory vs. experiential learning 117 5.6.2 The problem of priors 118 5.6.3 Discussion 120 5.7 Why does it work?* 121 5.7.1 Derivation of the knowledge gradient formula 121 5.8 Bibliographic notes 125 Problems 126 6 Bandit problems 139 6.1 The theory and practice of Gittins indices 141 6.1.1 Gittins indices in the beta-Bernoulli model 142 6.1.2 Gittins indices in the normal-normal model 145 6.1.3 Approximating Gittins indices 147 6.2 Variations of bandit problems 148 6.3 Upper confidence bounding 149 6.4 The knowledge gradient for bandit problems 151 6.4.1 The basic idea 151 6.4.2 Some experimental comparisons 153 6.4.3 Non-normal models 156 6.5 Bibliographic notes 157 Problems 157 7 Elements of a learning problem 163 7.1 The states of our system 164 7.2 Types of decisions 166 7.3 Exogenous information 167 7.4 Transition functions 168 7.5 Objective functions 168 7.5.1 Designing versus controlling 168 7.5.2 Measurement costs 170 7.5.3 Objectives 170 7.6 Evaluating policies 175 7.7 Discussion 177 7.8 Bibliographic notes 178 Problems 178 8 Linear belief models 181 8.1 Applications 182 8.1.1 Maximizing ad clicks 182 8.1.2 Dynamic pricing 184 8.1.3 Housing loans 184 8.1.4 Optimizing dose response 185 8.2 A brief review of linear regression 186 8.2.1 The normal equations 186 8.2.2 Recursive least squares 187 8.2.3 A Bayesian interpretation 188 8.2.4 Generating a prior 189 8.3 The knowledge gradient for a linear model 191 8.4 Application to drug discovery 192 8.5 Application to dynamic pricing 196 8.6 Bibliographic notes 200 Problems 200 9 Subset selection problems 203 9.1 Applications 205 9.2 Choosing a subset using ranking and selection 206 9.2.1 Setting prior means and variances 207 9.2.2 Two strategies for setting prior covariances 208 9.3 Larger sets 209 9.3.1 Using simulation to reduce the problem size 210 9.3.2 Computational issues 212 9.3.3 Experiments 213 9.4 Very large sets 214 9.5 Bibliographic notes 216 Problems 216 10 Optimizing a scalar function 219 10.1 Deterministic measurements 219 10.2 Stochastic measurements 223 10.2.1 The model 223 10.2.2 Finding the posterior distribution 224 10.2.3 Choosing the measurement 226 10.2.4 Discussion 229 10.3 Bibliographic notes 229 Problems 229 11 Optimal bidding 231 11.1 Modeling customer demand 233 11.1.1 Some valuation models 233 11.1.2 The logit model 234 11.2 Bayesian modeling for dynamic pricing 237 11.2.1 A conjugate prior for choosing between two demand curves 237 11.2.2 Moment matching for non-conjugate problems 239 11.2.3 An approximation for the logit model 242 11.3 Bidding strategies 244 11.3.1 An idea from multi-armed bandits 245 11.3.2 Bayes-greedy bidding 245 11.3.3 Numerical illustrations 247 11.4 Why does it work?* 251 11.4.1 Moment matching for Pareto prior 251 11.4.2 Approximating the logistic expectation 252 11.5 Bibliographic notes 253 Problems 254 12 Stopping problems 255 12.1 Sequential probability ratio test 255 12.2 The secretary problem 260 12.2.1 Setup 261 12.2.2 Solution 263 12.3 Bibliographic notes 266 Problems 266 13 Active learning in statistics 269 13.1 Deterministic policies 270 13.2 Sequential policies for classification 274 13.2.1 Uncertainty sampling 274 13.2.2 Query by committee 275 13.2.3 Expected error reduction 276 13.3 A variance minimizing policy 277 13.4 Mixtures of Gaussians 279 13.4.1 Estimating parameters 280 13.4.2 Active learning 281 13.5 Bibliographic notes 283 14 Simulation optimization 285 14.1 Indifference zone selection 287 14.1.1 Batch procedures 288 14.1.2 Sequential procedures 290 14.1.3 The 0-1 procedure: connection to linear loss 291 14.2 Optimal computing budget allocation 292 14.2.1 Indifference-zone version 293 14.2.2 Linear loss version 294 14.2.3 When does it work? 295 14.3 Model-based simulated annealing 296 14.4 Other areas of simulation optimization 298 14.5 Bibliographic notes 299 15 Learning in mathematical programming 301 15.1 Applications 303 15.1.1 Piloting a hot air balloon 303 15.1.2 Optimizing a portfolio 308 15.1.3 Network problems 309 15.1.4 Discussion 313 15.2 Learning on graphs 313 15.3 Alternative edge selection policies 316 15.4 Learning costs for linear programs* 317 15.5 Bibliographic notes 324 16 Optimizing over continuous measurements 325 16.1 The belief model 327 16.1.1 Updating equations 328 16.1.2 Parameter estimation 330 16.2 Sequential kriging optimization 332 16.3 The knowledge gradient for continuous parameters* 334 16.3.1 Maximizing the knowledge gradient 334 16.3.2 Approximating the knowledge gradient 335 16.3.3 The gradient of the knowledge gradient 336 16.3.4 Maximizing the knowledge gradient 338 16.3.5 The KGCP policy 339 16.4 Efficient global optimization 340 16.5 Experiments 341 16.6 Extension to higher dimensional problems 342 16.7 Bibliographic notes 343 17 Learning with a physical state 345 17.1 Introduction to dynamic programming 347 17.1.1 Approximate dynamic programming 348 17.1.2 The exploration vs. exploitation problem 350 17.1.3 Discussion 351 17.2 Some heuristic learning policies 352 17.3 The local bandit approximation 353 17.4 The knowledge gradient in dynamic programming 355 17.4.1 Generalized learning using basis functions 355 17.4.2 The knowledge gradient 358 17.4.3 Experiments 361 17.5 An expected improvement policy 363 17.6 Bibliographic notes 364 Index 379

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Book SynopsisClinical trials tasks and activities are widely diverse and require certain skill sets to both plan and execute. This book provides professionals in the field of clinical research with valuable information on the challenging issues of the design, execution, and management of clinical trials, and how to resolve these issues effectively. It discusses key obstacles such as challenges to patient recruitment, investigator and study site selection, and dealing with compliance issues. Through practical examples, professionals working with medical device clinical trials will discover the appropriate steps to take.Table of ContentsList of Abbreviations xi Preface xiii Acknowledgments xvii 1. Challenges to the Design of Clinical Study 1 Development of Clinical SOPs 3 Selection of Study Patients, Investigators, and Study Sites 3 Definition of Enrolled Subjects in a Clinical Study 7 Definition of the Investigational Device System 7 Research Contract Challenges 7 Review of Literature 9 Challenges to the Design of the Study Protocol, Statistical Analysis Plan (SAP), and Selection of Study Endpoints 11 Masking or Blinding 12 Primary and Secondary Outcomes 14 Selection of Study Endpoints 14 Differences between the Primary Endpoint in FDA and CE Mark Studies 15 SAP and Study Endpoints 15 Components of the SAP for Clinical Trials 17 Roles and Responsibilities of the Clinical Personnel in Completing the Study Protocol 19 Changing the Primary Outcome during the Conduct of the Study 20 Definition of Primary and Secondary Endpoints 22 Combined “Composite” Endpoints 22 Surrogate Endpoints 23 Reducing the Study’s Sample Size 25 Statistical Terms to Define Endpoint Measurements 25 Reporting Results of Clinical Trials 28 Superiority and Equivalence Trials 30 Subgroup Analysis 33 Challenges to ICF 35 Risk/Benefit Analysis 41 2. Challenges to Managing the Study 43 Enhancing Patient Enrollment by Relaxation of Study Criteria 45 Compliance with the Study Protocol 46 Challenges Associated with Data Accuracy and Completeness 47 Data Analysis 49 Data Integrity 55 Criteria for Using Meta-Analysis Studies 56 Who Should have Access to Clinical Trial Records 57 Managing Study Data and Quality Assurance 58 Missing Data Analysis 59 Examination of Data across Study Sites 60 Challenges to Adverse Event Reporting 62 Adverse Event Coding Systems 66 Protocol Deviation Report 68 Adverse Event Reporting in Final Study Clinical Report 68 Difference between the US and EU Definitions and Reporting of Adverse Events 69 Adverse Event Reporting Challenges 69 Minimization of Bias in Clinical Trials 69 3. Selection of Historic Controls 71 Types of Control Group in Medical Device Clinical Trials 73 Purpose of Control Group 73 Use of Placebo Control 74 Advantages of Randomized Control Clinical Trials 74 Disadvantages of Randomized Control Clinical Trials 74 Commonly Used Pivotal Designs 75 Definition of Historic Control 77 Objective Performance Criteria (OPC) 78 Examples of Clinical Studies with Historic Controls 80 LACI Clinical Study 80 Left Ventricular Assist Devices 86 Summary of Clinical Studies 88 Summary of Recommendations for Historic Control 96 4. Fraud and Misconduct in Clinical Trials 99 Fraud and Misconduct in Clinical Trials 100 Warning Signs of Fraud 101 Tips for Detecting Serious Misconduct 102 False Claims Act 102 Fraud Prevention 103 Policy on Handling Complaints of Misconduct 103 Reporting Research Misconduct 104 Bioresearch Monitoring Information System (BMIS) 104 5. Challenges to the Regulation of Medical Device 107 Determination of 510(K) Devices 108 510(K) “Substantial Equivalence Decision Making Process” 111 Determination of Nonsignificant Risk Devices (NSR) 111 Similarities and Differences between Medical Device and Drug Regulations in Clinical Trials 112 Definitions of Drugs and Devices 113 Combination Products 126 FDA–Sponsor Meetings 129 BIMO Inspection 130 Investigator-Initiated Clinical Trials 132 6. Challenges of Global Clinical Studies and the CE Mark Process 137 Global Trial Considerations 138 Global Harmonization Task Force Challenges 142 FDA Recommendations on Acceptance of Foreign Clinical Sites 143 Operational Tips on Conductance of Global Clinical Trials 143 CE Mark Process and Challenges 146 International Standard ISO 14155 148 Differences between FDA and CE Mark Clinical Trials 157 Challenges to CE Mark Studies 160 7. Challenging FDA PMA Cases 163 PMA P970029 (TMR 2000 Holmium Laser System) 164 PMA P040012 Carotid Stenting for Treating Carotid Disease 175 Historic Control Assumptions 175 Use of Angiographic Late Loss as Primary Endpoint in Drug-Eluting Stent PMA P070015 (Xience V DES) 186 8. Bioethics in Clinical Research 199 Bioethical Challenges in Clinical Studies 200 Good Clinical Practice for the Investigator 201 WHO Principles of GCP 202 Guidelines and Ethical Principles 204 IRB Review Process 206 Glossary of Clinical, CE Mark, and Statistical Terms 211 References 217 Index 221

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Book SynopsisPraise for the First Edition Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! This beautiful book fills a gap in the libraries of OR specialists and practitioners. Computing Reviews This new edition showcases a focus on modeling and computation for complex classes of approximate dynamic programming problems Understanding approximate dynamic programming (ADP) is vital in order to develop practical and high-quality solutions to complex industrial problems, particularly when those problems involve making decisions in the presence of uncertainty. Approximate Dynamic Programming, Second Edition uniquely integrates four distinct disciplinesMarkov decision processes, mathematical programming, simulation, and statisticsto demonstrate how to successfully approach, model, and solve a wide range of real-life problems using ADP. The book continues to bridge the gap betTable of ContentsPreface to the Second Edition xi Preface to the First Edition xv Acknowledgments xvii 1 The Challenges of Dynamic Programming 1 1.1 A Dynamic Programming Example: A Shortest Path Problem, 2 1.2 The Three Curses of Dimensionality, 3 1.3 Some Real Applications, 6 1.4 Problem Classes, 11 1.5 The Many Dialects of Dynamic Programming, 15 1.6 What Is New in This Book?, 17 1.7 Pedagogy, 19 1.8 Bibliographic Notes, 22 2 Some Illustrative Models 25 2.1 Deterministic Problems, 26 2.2 Stochastic Problems, 31 2.3 Information Acquisition Problems, 47 2.4 A Simple Modeling Framework for Dynamic Programs, 50 2.5 Bibliographic Notes, 54 Problems, 54 3 Introduction to Markov Decision Processes 57 3.1 The Optimality Equations, 58 3.2 Finite Horizon Problems, 65 3.3 Infinite Horizon Problems, 66 3.4 Value Iteration, 68 3.5 Policy Iteration, 74 3.6 Hybrid Value-Policy Iteration, 75 3.7 Average Reward Dynamic Programming, 76 3.8 The Linear Programming Method for Dynamic Programs, 77 3.9 Monotone Policies*, 78 3.10 Why Does It Work?**, 84 3.11 Bibliographic Notes, 103 Problems, 103 4 Introduction to Approximate Dynamic Programming 111 4.1 The Three Curses of Dimensionality (Revisited), 112 4.2 The Basic Idea, 114 4.3 Q-Learning and SARSA, 122 4.4 Real-Time Dynamic Programming, 126 4.5 Approximate Value Iteration, 127 4.6 The Post-Decision State Variable, 129 4.7 Low-Dimensional Representations of Value Functions, 144 4.8 So Just What Is Approximate Dynamic Programming?, 146 4.9 Experimental Issues, 149 4.10 But Does It Work?, 155 4.11 Bibliographic Notes, 156 Problems, 158 5 Modeling Dynamic Programs 167 5.1 Notational Style, 169 5.2 Modeling Time, 170 5.3 Modeling Resources, 174 5.4 The States of Our System, 178 5.5 Modeling Decisions, 187 5.6 The Exogenous Information Process, 189 5.7 The Transition Function, 198 5.8 The Objective Function, 206 5.9 A Measure-Theoretic View of Information**, 211 5.10 Bibliographic Notes, 213 Problems, 214 6 Policies 221 6.1 Myopic Policies, 224 6.2 Lookahead Policies, 224 6.3 Policy Function Approximations, 232 6.4 Value Function Approximations, 235 6.5 Hybrid Strategies, 239 6.6 Randomized Policies, 242 6.7 How to Choose a Policy?, 244 6.8 Bibliographic Notes, 247 Problems, 247 7 Policy Search 249 7.1 Background, 250 7.2 Gradient Search, 253 7.3 Direct Policy Search for Finite Alternatives, 256 7.4 The Knowledge Gradient Algorithm for Discrete Alternatives, 262 7.5 Simulation Optimization, 270 7.6 Why Does It Work?**, 274 7.7 Bibliographic Notes, 285 Problems, 286 8 Approximating Value Functions 289 8.1 Lookup Tables and Aggregation, 290 8.2 Parametric Models, 304 8.3 Regression Variations, 314 8.4 Nonparametric Models, 316 8.5 Approximations and the Curse of Dimensionality, 325 8.6 Why Does It Work?**, 328 8.7 Bibliographic Notes, 333 Problems, 334 9 Learning Value Function Approximations 337 9.1 Sampling the Value of a Policy, 337 9.2 Stochastic Approximation Methods, 347 9.3 Recursive Least Squares for Linear Models, 349 9.4 Temporal Difference Learning with a Linear Model, 356 9.5 Bellman’s Equation Using a Linear Model, 358 9.6 Analysis of TD(0), LSTD, and LSPE Using a Single State, 364 9.7 Gradient-Based Methods for Approximate Value Iteration*, 366 9.8 Least Squares Temporal Differencing with Kernel Regression*, 371 9.9 Value Function Approximations Based on Bayesian Learning*, 373 9.10 Why Does It Work*, 376 9.11 Bibliographic Notes, 379 Problems, 381 10 Optimizing While Learning 383 10.1 Overview of Algorithmic Strategies, 385 10.2 Approximate Value Iteration and Q-Learning Using Lookup Tables, 386 10.3 Statistical Bias in the Max Operator, 397 10.4 Approximate Value Iteration and Q-Learning Using Linear Models, 400 10.5 Approximate Policy Iteration, 402 10.6 The Actor–Critic Paradigm, 408 10.7 Policy Gradient Methods, 410 10.8 The Linear Programming Method Using Basis Functions, 411 10.9 Approximate Policy Iteration Using Kernel Regression*, 413 10.10 Finite Horizon Approximations for Steady-State Applications, 415 10.11 Bibliographic Notes, 416 Problems, 418 11 Adaptive Estimation and Stepsizes 419 11.1 Learning Algorithms and Stepsizes, 420 11.2 Deterministic Stepsize Recipes, 425 11.3 Stochastic Stepsizes, 433 11.4 Optimal Stepsizes for Nonstationary Time Series, 437 11.5 Optimal Stepsizes for Approximate Value Iteration, 447 11.6 Convergence, 449 11.7 Guidelines for Choosing Stepsize Formulas, 451 11.8 Bibliographic Notes, 452 Problems, 453 12 Exploration Versus Exploitation 457 12.1 A Learning Exercise: The Nomadic Trucker, 457 12.2 An Introduction to Learning, 460 12.3 Heuristic Learning Policies, 464 12.4 Gittins Indexes for Online Learning, 470 12.5 The Knowledge Gradient Policy, 477 12.6 Learning with a Physical State, 482 12.7 Bibliographic Notes, 492 Problems, 493 13 Value Function Approximations for Resource Allocation Problems 497 13.1 Value Functions versus Gradients, 498 13.2 Linear Approximations, 499 13.3 Piecewise-Linear Approximations, 501 13.4 Solving a Resource Allocation Problem Using Piecewise-Linear Functions, 505 13.5 The SHAPE Algorithm, 509 13.6 Regression Methods, 513 13.7 Cutting Planes*, 516 13.8 Why Does It Work?**, 528 13.9 Bibliographic Notes, 535 Problems, 536 14 Dynamic Resource Allocation Problems 541 14.1 An Asset Acquisition Problem, 541 14.2 The Blood Management Problem, 547 14.3 A Portfolio Optimization Problem, 557 14.4 A General Resource Allocation Problem, 560 14.5 A Fleet Management Problem, 573 14.6 A Driver Management Problem, 580 14.7 Bibliographic Notes, 585 Problems, 586 15 Implementation Challenges 593 15.1 Will ADP Work for Your Problem?, 593 15.2 Designing an ADP Algorithm for Complex Problems, 594 15.3 Debugging an ADP Algorithm, 596 15.4 Practical Issues, 597 15.5 Modeling Your Problem, 602 15.6 Online versus Offline Models, 604 15.7 If It Works, Patent It!, 606 Bibliography 607 Index 623

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Book SynopsisPraise for the Second Edition This book is an excellent introduction to the wide field of boundary value problems.Journal of Engineering Mathematics No doubt this textbook will be useful for both students and research workers.Mathematical Reviews A new edition of the highly-acclaimed guide to boundary value problems, now featuring modern computational methods and approximation theory Green''s Functions and Boundary Value Problems, Third Edition continues the tradition of the two prior editions by providing mathematical techniques for the use of differential and integral equations to tackle important problems in applied mathematics, the physical sciences, and engineering. This new edition presents mathematical concepts and quantitative tools that are essential for effective use of modern computational methods that play a key role in the practical solution of boundary value problems. With a careful blend of theory and applications, the authors succeTrade Review Table of ContentsPreface to Third Edition. Preface to Second Edition. Preface to First Edition. 0 Preliminaries. 0.1 Heat Conduction. 0.2 Diffusion. 0.3 Reaction-Diffusion Problems. 0.4 The Impulse-Momentum Law: The Motion of Rods and Strings. 0.5 Alternative Formulations of Physical Problems. 0.6 Notes on Convergence. 0.7 The Lebesgue Integral. 1 Green’s Functions (Intuitive Ideas). 1.1 Introduction and General Comments. 1.2 The Finite Rod. 1.3 The Maximum Principle. 1.4 Examples of Green’s Functions. 2 The Theory of Distributions. 2.1 Basic Ideas, Definitions, and Examples. 2.2 Convergence of Sequences and Series of Distributions. 2.3 Fourier Series. 2.4 Fourier Transforms and Integrals. 2.5 Differential Equations in Distributions. 2.6 Weak Derivatives and Sobolev Spaces. 3 One-Dimensional Boundary Value Problems. 3.1 Review. 3.2 Boundary Value Problems for Second-Order Equations. 3.3 Boundary Value Problems for Equations of Order p. 3.4 Alternative Theorems. 3.5 Modified Green's Functions. 4 Hilbert and Banach Spaces. 4.1 Functions and Transformations. 4.2 Linear Spaces. 4.3 Metric Spaces, Normed Linear Spaces, and Banach Spaces. 4.4 Contractions and the Banach Fixed-Point Theorem. 4.5 Hilbert Spaces and the Projection Theorem. 4.6 Separable Hilbert Spaces and Orthonormal Bases. 4.7 Linear Functionals and the Riesz Representation Theorem. 4.8 The Hahn-Banach Theorem and Reflexive Banach Spaces. 5 Operator Theory. 5.1 Basic Ideas and Examples. 5.2 Closed Operators. 5.3 Invertibility: The State of an Operator. 5.4 Adjoint Operators. 5.5 Solvability Conditions. 5.6 The Spectrum of an Operator. 5.7 Compact Operators. 5.8 Extremal Properties of Operators. 5.9 The Banach-Schauder and Banach-Steinhaus Theorems. 6 Integral Equations. 6.1 Introduction. 6.2 Fredholm Integral Equations. 6.3 The Spectrum of a Self-Adjoint Compact Operator. 6.4 The Inhomogeneous Equation. 6.5 Variational Principles and Related Approximation Methods. 7 Spectral Theory of Second-Order Differential Operators. 7.1 Introduction; The Regular Problem. 7.2 Weyl’s Classification of Singular Problems. 7.3 Spectral Problems with a Continuous Spectrum. 8 Partial Differential Equations. 8.1 Classification of Partial Differential Equations. 8.2 Well-Posed Problems for Hyperbolic and Parabolic Equations. 8.3 Elliptic Equations. 8.4 Variational Principles for Inhomogeneous Problems. 8.5 The Lax-Milgram Theorem. 9 Nonlinear Problems. 9.1 Introduction and Basic Fixed-Point Techniques. 9.2 Branching Theory. 9.3 Perturbation Theory for Linear Problems. 9.4 Techniques for Nonlinear Problems. 9.5 The Stability of the Steady State. 10 Approximation Theory and Methods. 10.1 Nonlinear Analysis Tools for Banach Spaces. 10.2 Best and Near-Best Approximation in Banach Spaces. 10.3 Overview of Sobolev and Besov Spaces. 10.4 Applications to Nonlinear Elliptic Equations. 10.5 Finite Element and Related Discretization Methods. 10.6 Iterative Methods for Discretized Linear Equations. 10.7 Methods for Nonlinear Equations. Index.

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Book SynopsisA practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes oTable of ContentsPreface xv Acknowledgments xvii List of Figures Xix List of Tables xxv PART I PRELIMINARIES 1 Introduction 3 1.1 Bayesian Inference 4 1.2 Bayesian Hierarchy of Estimation Methods 5 1.3 Scope of This Text 6 1.3.1 Objective 6 1.3.2 Chapter Overview and Prerequisites 6 1.4 Modeling and Simulation with MATLAB® 8 References 9 2 Preliminary Mathematical Concepts 11 2.1 A Very Brief Overview of Matrix Linear Algebra 11 2.1.1 Vector and Matrix Conventions and Notation 11 2.1.2 Sums and Products 12 2.1.3 Matrix Inversion 13 2.1.4 Block Matrix Inversion 14 2.1.5 Matrix Square Root 15 2.2 Vector Point Generators 16 2.3 Approximating Nonlinear Multidimensional Functions with Multidimensional Arguments 19 2.3.1 Approximating Scalar Nonlinear Functions 19 2.3.2 Approximating Multidimensional Nonlinear Functions 23 2.4 Overview of Multivariate Statistics 29 2.4.1 General Definitions 29 2.4.2 The Gaussian Density 32 References 40 3 General Concepts of Bayesian Estimation 42 3.1 Bayesian Estimation 43 3.2 Point Estimators 43 3.3 Introduction to Recursive Bayesian Filtering of Probability Density Functions 46 3.4 Introduction to Recursive Bayesian Estimation of the State Mean and Covariance 49 3.4.1 State Vector Prediction 50 3.4.2 State Vector Update 51 3.5 Discussion of General Estimation Methods 55 References 55 4 Case Studies: Preliminary Discussions 56 4.1 The Overall Simulation/Estimation/Evaluation Process 57 4.2 A Scenario Simulator for Tracking a Constant Velocity Target Through a DIFAR Buoy Field 58 4.2.1 Ship Dynamics Model 58 4.2.2 Multiple Buoy Observation Model 59 4.2.3 Scenario Specifics 59 4.3 DIFAR Buoy Signal Processing 62 4.4 The DIFAR Likelihood Function 67 References 69 PART II THE GAUSSIAN ASSUMPTION: A FAMILY OF KALMAN FILTER ESTIMATORS 5 The Gaussian Noise Case: Multidimensional Integration of Gaussian-Weighted Distributions 73 5.1 Summary of Important Results From Chapter 3 74 5.2 Derivation of the Kalman Filter Correction (Update) Equations Revisited 76 5.3 The General Bayesian Point Prediction Integrals for Gaussian Densities 78 5.3.1 Refining the Process Through an Affine Transformation 80 5.3.2 General Methodology for Solving Gaussian-Weighted Integrals 82 References 85 6 The Linear Class of Kalman Filters 86 6.1 Linear Dynamic Models 86 6.2 Linear Observation Models 87 6.3 The Linear Kalman Filter 88 6.4 Application of the LKF to DIFAR Buoy Bearing Estimation 88 References 92 7 The Analytical Linearization Class of Kalman Filters: The Extended Kalman Filter 93 7.1 One-Dimensional Consideration 93 7.1.1 One-Dimensional State Prediction 94 7.1.2 One-Dimensional State Estimation Error Variance Prediction 95 7.1.3 One-Dimensional Observation Prediction Equations 96 7.1.4 Transformation of One-Dimensional Prediction Equations 96 7.1.5 The One-Dimensional Linearized EKF Process 98 7.2 Multidimensional Consideration 98 7.2.1 The State Prediction Equation 99 7.2.2 The State Covariance Prediction Equation 100 7.2.3 Observation Prediction Equations 102 7.2.4 Transformation of Multidimensional Prediction Equations 103 7.2.5 The Linearized Multidimensional Extended Kalman Filter Process 105 7.2.6 Second-Order Extended Kalman Filter 105 7.3 An Alternate Derivation of the Multidimensional Covariance Prediction Equations 107 7.4 Application of the EKF to the DIFAR Ship Tracking Case Study 108 7.4.1 The Ship Motion Dynamics Model 108 7.4.2 The DIFAR Buoy Field Observation Model 109 7.4.3 Initialization for All Filters of the Kalman Filter Class 111 7.4.4 Choosing a Value for the Acceleration Noise 112 7.4.5 The EKF Tracking Filter Results 112 References 114 8 The Sigma Point Class: The Finite Difference Kalman Filter 115 8.1 One-Dimensional Finite Difference Kalman Filter 116 8.1.1 One-Dimensional Finite Difference State Prediction 116 8.1.2 One-Dimensional Finite Difference State Variance Prediction 117 8.1.3 One-Dimensional Finite Difference Observation Prediction Equations 118 8.1.4 The One-Dimensional Finite Difference Kalman Filter Process 118 8.1.5 Simplified One-Dimensional Finite Difference Prediction Equations 118 8.2 Multidimensional Finite Difference Kalman Filters 120 8.2.1 Multidimensional Finite Difference State Prediction 120 8.2.2 Multidimensional Finite Difference State Covariance Prediction 123 8.2.3 Multidimensional Finite Difference Observation Prediction Equations 124 8.2.4 The Multidimensional Finite Difference Kalman Filter Process 125 8.3 An Alternate Derivation of the Multidimensional Finite Difference Covariance Prediction Equations 125 References 127 9 The Sigma Point Class: The Unscented Kalman Filter 128 9.1 Introduction to Monomial Cubature Integration Rules 128 9.2 The Unscented Kalman Filter 130 9.2.1 Background 130 9.2.2 The UKF Developed 131 9.2.3 The UKF State Vector Prediction Equation 134 9.2.4 The UKF State Vector Covariance Prediction Equation 134 9.2.5 The UKF Observation Prediction Equations 135 9.2.6 The Unscented Kalman Filter Process 135 9.2.7 An Alternate Version of the Unscented Kalman Filter 135 9.3 Application of the UKF to the DIFAR Ship Tracking Case Study 137 References 138 10 The Sigma Point Class: The Spherical Simplex Kalman Filter 140 10.1 One-Dimensional Spherical Simplex Sigma Points 141 10.2 Two-Dimensional Spherical Simplex Sigma Points 142 10.3 Higher Dimensional Spherical Simplex Sigma Points 144 10.4 The Spherical Simplex Kalman Filter 144 10.5 The Spherical Simplex Kalman Filter Process 145 10.6 Application of the SSKF to the DIFAR Ship Tracking Case Study 146 Reference 147 11 The Sigma Point Class: The Gauss–Hermite Kalman Filter 148 11.1 One-Dimensional Gauss–Hermite Quadrature 149 11.2 One-Dimensional Gauss–Hermite Kalman Filter 153 11.3 Multidimensional Gauss–Hermite Kalman Filter 155 11.4 Sparse Grid Approximation for High Dimension/High Polynomial Order 160 11.5 Application of the GHKF to the DIFAR Ship Tracking Case Study 163 References 163 12 The Monte Carlo Kalman Filter 164 12.1 The Monte Carlo Kalman Filter 167 Reference 167 13 Summary of Gaussian Kalman Filters 168 13.1 Analytical Kalman Filters 168 13.2 Sigma Point Kalman Filters 170 13.3 A More Practical Approach to Utilizing the Family of Kalman Filters 174 References 175 14 Performance Measures for the Family of Kalman Filters 176 14.1 Error Ellipses 176 14.1.1 The Canonical Ellipse 177 14.1.2 Determining the Eigenvalues of P 178 14.1.3 Determining the Error Ellipse Rotation Angle 179 14.1.4 Determination of the Containment Area 180 14.1.5 Parametric Plotting of Error Ellipse 181 14.1.6 Error Ellipse Example 182 14.2 Root Mean Squared Errors 182 14.3 Divergent Tracks 183 14.4 Cramer–Rao Lower Bound 184 14.4.1 The One-Dimensional Case 184 14.4.2 The Multidimensional Case 186 14.4.3 A Recursive Approach to the CRLB 186 14.4.4 The Cramer–Rao Lower Bound for Gaussian Additive Noise 190 14.4.5 The Gaussian Cramer–Rao Lower Bound with Zero Process Noise 191 14.4.6 The Gaussian Cramer–Rao Lower Bound with Linear Models 191 14.5 Performance of Kalman Class DIFAR Track Estimators 192 References 198 PART III MONTE CARLO METHODS 15 Introduction to Monte Carlo Methods 201 15.1 Approximating a Density From a Set of Monte Carlo Samples 202 15.1.1 Generating Samples from a Two-Dimensional Gaussian Mixture Density 202 15.1.2 Approximating a Density by Its Multidimensional Histogram 202 15.1.3 Kernel Density Approximation 204 15.2 General Concepts Importance Sampling 210 15.3 Summary 215 References 216 16 Sequential Importance Sampling Particle Filters 218 16.1 General Concept of Sequential Importance Sampling 218 16.2 Resampling and Regularization (Move) for SIS Particle Filters 222 16.2.1 The Inverse Transform Method 222 16.2.2 SIS Particle Filter with Resampling 226 16.2.3 Regularization 227 16.3 The Bootstrap Particle Filter 230 16.3.1 Application of the BPF to DIFAR Buoy Tracking 231 16.4 The Optimal SIS Particle Filter 233 16.4.1 Gaussian Optimal SIS Particle Filter 235 16.4.2 Locally Linearized Gaussian Optimal SIS Particle Filter 236 16.5 The SIS Auxiliary Particle Filter 238 16.5.1 Application of the APF to DIFAR Buoy Tracking 242 16.6 Approximations to the SIS Auxiliary Particle Filter 243 16.6.1 The Extended Kalman Particle Filter 243 16.6.2 The Unscented Particle Filter 243 16.7 Reducing the Computational Load Through Rao-Blackwellization 245 References 245 17 The Generalized Monte Carlo Particle Filter 247 17.1 The Gaussian Particle Filter 248 17.2 The Combination Particle Filter 250 17.2.1 Application of the CPF–UKF to DIFAR Buoy Tracking 252 17.3 Performance Comparison of All DIFAR Tracking Filters 253 References 255 PART IV ADDITIONAL CASE STUDIES 18 A Spherical Constant Velocity Model for Target Tracking in Three Dimensions 259 18.1 Tracking a Target in Cartesian Coordinates 261 18.1.1 Object Dynamic Motion Model 262 18.1.2 Sensor Data Model 263 18.1.3 GaussianTracking Algorithms for a Cartesian StateVector 264 18.2 Tracking a Target in Spherical Coordinates 265 18.2.1 State Vector Position and Velocity Components in Spherical Coordinates 266 18.2.2 Spherical State Vector Dynamic Equation 267 18.2.3 Observation Equations with a Spherical State Vector 270 18.2.4 GaussianTracking Algorithms for a Spherical StateVector 270 18.3 Implementation of Cartesian and Spherical Tracking Filters 273 18.3.1 Setting Values for q 273 18.3.2 Simulating Radar Observation Data 274 18.3.3 Filter Initialization 276 18.4 Performance Comparison for Various Estimation Methods 278 18.4.1 Characteristics of the Trajectories Used for Performance Analysis 278 18.4.2 Filter Performance Comparisons 282 18.5 Some Observations and Future Considerations 293 APPENDIX 18.A Three-Dimensional Constant Turn Rate Kinematics 294 18.A.1 General Velocity Components for Constant Turn Rate Motion 294 18.A.2 General Position Components for Constant Turn Rate Motion 297 18.A.3 Combined Trajectory Transition Equation 299 18.A.4 Turn Rate Setting Based on a Desired Turn Acceleration 299 APPENDIX 18.B Three-Dimensional Coordinate Transformations 301 18.B.1 Cartesian-to-Spherical Transformation 302 18.B.2 Spherical-to-Cartesian Transformation 305 References 306 19 Tracking a Falling Rigid Body Using Photogrammetry 308 19.1 Introduction 308 19.2 The Process (Dynamic) Model for Rigid Body Motion 311 19.2.1 Dynamic Transition of the Translational Motion of a Rigid Body 311 19.2.2 Dynamic Transition of the Rotational Motion of a Rigid Body 313 19.2.3 Combined Dynamic Process Model 316 19.2.4 The Dynamic Process Noise Models 317 19.3 Components of the Observation Model 318 19.4 Estimation Methods 321 19.4.1 A Nonlinear Least Squares Estimation Method 321 19.4.2 An Unscented Kalman Filter Method 323 19.4.3 Estimation Using the Unscented Combination Particle Filter 325 19.4.4 Initializing the Estimator 326 19.5 The Generation of Synthetic Data 328 19.5.1 Synthetic Rigid Body Feature Points 328 19.5.2 Synthetic Trajectory 328 19.5.3 Synthetic Cameras 333 19.5.4 Synthetic Measurements 333 19.6 Performance Comparison Analysis 334 19.6.1 Filter Performance Comparison Methodology 335 19.6.2 Filter Comparison Results 338 19.6.3 Conclusions and Future Considerations 341 APPENDIX 19.A Quaternions Axis-Angle Vectors and Rotations 342 19.A.1 Conversions Between Rotation Representations 342 19.A.2 Representation of Orientation and Rotation 343 19.A.3 Point Rotations and Frame Rotations 344 References 345 20 Sensor Fusion Using Photogrammetric and Inertial Measurements 346 20.1 Introduction 346 20.2 The Process (Dynamic) Model for Rigid Body Motion 347 20.3 The Sensor Fusion Observational Model 348 20.3.1 The Inertial Measurement Unit Component of the Observation Model 348 20.3.2 The Photogrammetric Component of the Observation Model 350 20.3.3 The Combined Sensor Fusion Observation Model 351 20.4 The Generation of Synthetic Data 352 20.4.1 Synthetic Trajectory 352 20.4.2 Synthetic Cameras 352 20.4.3 Synthetic Measurements 352 20.5 Estimation Methods 354 20.5.1 Initial Value Problem Solver for IMU Data 354 20.6 Performance Comparison Analysis 357 20.6.1 Filter Performance Comparison Methodology 359 20.6.2 Filter Comparison Results 360 20.7 Conclusions 361 20.8 Future Work 362 References 364 Index 367

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Book SynopsisWhat are the chances? Find out in this entertaining exploration ofprobabilities in our everyday lives If there is anything you want to know, or remind yourself, about probabilities, then look no further than this comprehensive, yet wittily written and enjoyable, compendium of how to apply probability calculations in real-world situations. Keith Devlin, Stanford University, National Public Radio's Math Guy and author of The Math Gene and The Math Instinct A delightful guide to the sometimes counterintuitive discipline of probability. Olofsson points out major ideas here, explains classic puzzles there, and everywhere makes free use of witty vignettes to instruct and amuse. John Allen Paulos, Temple University, author of Innumeracy and A Mathematician Reads the Newspaper Beautifully written, with fascinating examples and tidbits of information. Olofsson gently and persuasively shows us how to think clearly abTable of ContentsPreface v 1 Computing Probabilities: Right Ways and Wrong Ways 1 The Probabilist 1 The Probabilist's Toys and Language 4 The Probabilist's Rule Book 9 Independence, Airplanes, and Russian Peasants 14 Conditional Probability, Swedish TV, and British Courts 20 Liar, Liar 24 Total Probability, Used Cars, and Tennis Matches 28 Combinatorics, Pastrami, and Poetry 33 The von Trapps and the Binomial Distribution 37 Final Word 43 2 Surprising Probabilities: When Intuition Struggles 45 Boys, Girls, Aces, and Colored Cards 45 Goats and Gloats 50 Happy Birthday 52 Typical Atypicalities 57 Strategies, Shopping, and Spaghetti Westerns 61 The British Snob and I 65 Final Word 70 3 Tiny Probabilities: Why Are They So Hard to Escape? 71 Probable Improbabilities 71 Saddam and I 75 Taking Tiny Risks 80 A Million-to-One Shot, Doc, Million to One! 82 Monsieur Poisson and the Mysterious Number 37 84 Clumps in Space 89 Final Word 91 4 Backward Probabilities: The Reverend Bayes to Our Rescue 93 Driving Miss Daisy 93 Bayes, Balls, and Boys (and Girls) 96 Bayes and My Green Card 98 Objection Your Honor 103 Final Word 112 5 Beyond Probabilities: What to Expect 115 Great Expectations 115 Good Things Come to Those Who Wait 123 Expect the Unexpected 129 Size Matters (and Length, and Age) 132 Deviant Behavior 138 Final Word 143 6 Inevitable Probabilities: Two Fascinating Mathematical Results 145 Alea Iacta Est, Over and Over 145 Even-Steven? The Law Misunderstood 149 Coin Tosses and Freeway Congestion 155 Let's Get Serious 162 Bells and Bread 166 How a Toronto Quincunx Changed My Life 171 Final Word 173 7 Gambling Probabilities: Why Donald Trump Is Richer than You 175 French Letters 175 Roulette: A Classy Way to Waste Your Money 179 Craps: Not so Dicey After All 184 Blackjack: Money for Mnemonics 187 Math for Losers 193 Win Money and Lose Friends 200 Final Word 210 8 Guessing Probabilities: Enter the Statisticians 211 Lies, Damned Lies, and Beautiful Lies? 211 4 out of 10 Like the President 19 Times out of 20 215 Polls Gone Wild 220 The Lawsuit and the Lurker 225 Football Players and Geyser Eruptions 230 Snooping in the Abbot’s Garden 237 Final Word 242 9 Faking Probabilities: Computer Simulation 245 Mahogany Dice and Modular Arithmetic 245 Random and Not-So-Random Digits 252 Number One Is Number One 253 Is Random Really Random? 256 Final Word 261 Index 263

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