Database design and theory Books

Book SynopsisIndividual-based models are widely used tool for ecology. This book provides the treatment of individual-based modeling and its use to develop theoretical understanding of how ecological systems work, an approach the authors call "individual-based ecology."Trade Review"The authors establish an effective and coherent framework for using individual-based modelling."--Nikita Y. Ratanov, Mathematical Reviews "An excellent book, which aims to invigorate individual-based modeling ... by providing a clear theoretical framework for the subject--which they term individual-based ecology (IBE)--and a step-by-step guide to creating individual-based models (IBMs) within this framework... I think this is a very timely book, and I recommend it to anyone new or old to the subject."--Richard Stillman, Quarterly Review of Biology "The book very successfully link[s] various 'universes' ranging from fundamental concepts in ecology and complex systems research to hands-on technical and recipe-like suggestions on how to build a model, illustrated with numerous, well-chosen examples."--Janine Bolliger, Landscape Ecology "For anyone who wants to know more about and possibly incorporate IBMs in his own research, this book provides plenty of advice and guidance on how to formulate, analyze, and use such models. If IBMs do ultimately reach the potential envisioned by the authors, their seminal book will have done much to contribute to that success."--Jim M. Cushing, Zentralblatt MATH "This book establishes an effective and coherent conceptual and technical framework for individual-based modeling with the objective to develop and illustrate an approach for addressing how individual behaviors and system dynamics emerge from lower-level traits."--Janine Bolliger, Landscape Ecology "Given the solid conceptual foundation of the book and the wide range of IBM applications in fish ecology, I think that many fish biologists will find this book very useful and I recommend it warmly."--Geir Huse, Fish and FisheriesTable of ContentsPreface xi Acknowledgments xv PART 1.MODELING 1 Chapter 1. Introduction 3 1.1 Why Individual-based Modeling and Ecology? 3 1.2 Linking Individual Traits and System Complexity: Three Examples 5 1.3 Individual-based Ecology 9 1.4 Early IBMs and Their Research Programs 11 1.5 What Makes a Model an IBM? 13 1.6 Status and Challenges of the Individual-based Approach 15 1.7 Conclusions and Outlook 19 Chapter 2. A Primer to Modeling 22 2.1 Introduction 22 2.2 Heuristics for Modeling 24 2.3 The Modeling Cycle 27 2.4 Summary and Discussion 36 Chapter 3. Pattern-oriented Modeling 38 3.1 Introduction 38 3.2 Why Patterns, and What Are Patterns? 40 3.3 The Tasks of Pattern-oriented Modeling 41 3.4 Discussion 48 PART 2.INDIVIDUAL-BASED ECOLOGY 51 Chapter 4. Theory in Individual-based Ecology 53 4.1 Introduction 53 4.2 Basis for Theory in IBE 55 4.3 Goals of IBE Theory 56 4.4 Theory Structure 58 4.5 Theory Development Cycle 60 4.6 Example: Development of Habitat Selection Theory for Trout 63 4.7 Summary and Discussion 68 Chapter 5. A Conceptual Framework for Designing Individual-based Models 71 5.1 Introduction 71 5.2 Emergence 73 5.3 Adaptive Traits and Behavior 79 5.4 Fitness 84 5.5 Prediction 91 5.6 Interaction 95 5.7 Sensing 98 5.8 Stochasticity 101 5.9 Collectives 105 5.10 Scheduling 109 5.11 Observation 116 5.12 Summary and Conclusions 117 5.13 Conceptual Design Checklist 119 Chapter 6. Examples 122 6.1 Introduction 122 6.2 Group and Social Behavior 125 6.3 Population Dynamics of Social Animals 145 6.4 Movement: Dispersal and Habitat Selection 163 6.5 Regulation of Hypothetical Populations 178 6.6 Comparison with Classical Models 187 6.7 Dynamics of Plant Populations and Communities 199 6.8 Structure of Communities and Ecosystems 218 6.9 Artificially Evolved Traits 234 6.10 Summary and Conclusions 242 PART 3.THE ENGINE ROOM 245 Chapter 7. Formulating Individual-based Models 247 7.1 Introduction 247 7.2 Contents of an IBM Formulation 248 7.3 Formulating an IBM's Spatial Elements 249 7.4 Formulating Logical and Probabilistic Rules 253 7.5 Formulating Adaptive Traits 255 7.6 Controlling Uncertainty 260 7.7 Using Object-oriented Design and Description 262 7.8 Using Mechanistic and Discrete Mathematics 264 7.9 Designing Superindividuals 266 7.10 Summary and Conclusions 269 Chapter 8. Software for Individual-based Models 270 8.1 Introduction 270 8.2 The Importance of Software Design for IBMs 273 8.3 Software Terminology and Concepts 274 8.4 Software Platforms 279 8.5 Software Testing 288 8.6 Moving Software Development Forward 294 8.7 Important Implementation Techniques 301 8.8 Some Favorite Software Myths 306 8.9 Summary and Conclusions 308 Chapter 9. Analyzing Individual-based Models 312 9.1 Introduction 312 9.2 Steps in Analyzing an IBM 313 9.3 General Strategies for Analyzing IBMs 315 9.4 Techniques for Analyzing IBMs 319 9.5 Statistical Analysis 327 9.6 Sensitivity and Uncertainty Analysis 335 9.7 Robustness Analysis 336 9.8 Parameterization 341 9.9 Independent Predictions 345 9.10 Summary and Conclusions 346 Chapter 10. Communicating Individual-based Models and Research 349 10.1 Introduction 349 10.2 Types of IBE Work to Communicate 350 10.3 Complete and Efficient Model Description 351 10.4 Common Review Comments 354 10.5 Visual Communication of Executable Models 356 10.6 Communicating Software 358 10.7 Summary and Conclusions 359 PART 4.CONCLUSIONS AND OUTLOOK 363 Chapter 11. Using Analytical Models in Individual-based Ecology 365 11.1 Introduction 365 11.2 Classifications of Ecological Models 366 11.3 Benefits of Analytical Models 368 11.4 Analytical Approximation of IBMs 369 11.5 Using Analytical Models to Understand and Analyze IBMs 372 11.6 Summary and Discussion 379 Chapter 12. Conclusions and Outlook for Individual-based Ecology 380 12.1 Introduction 380 12.2 Why Do We Need IBE? 381 12.3 How Is IBE Different From Traditional Ecology? 382 12.4 What Can Ecology Contribute to the Science of Complex Systems? 387 12.5 A Visit to the Individual-based Ecology Laboratory 388 Glossary 391 References 395 Index 421

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Book SynopsisDesigned to help move from verbal to mathematical to computational representations in economic modeling, this book is organized around economic topics as macroeconomics, microeconomics, and finance. It employs software systems, including MATLAB, Mathematica, GAMS, the nonlinear programming solver in Excel, and the database systems in Access.Trade Review"Important and useful... [T]his book represents an excellent way to learn computational economics, doing it."--Pietro Terna, Journal of Artificial Societies and Social SimulationTable of ContentsPreface ix Introduction 1 PART I: Once Over Lightly ... Growth Chapter 1: Growth Model in Excel 9 Finance Chapter 2: Neural Nets in Excel 25 Microeconomics Chapter 3: PartIal Equilibrium in Mathematica 37 Chapter 4: Transportation in GAMS 55 Database Chapter 5: Databases in Access 67 Finance Chapter 6: Thrift in GAMS (with Genevieve Solomon) 91 Chapter 7: Portfolio Model in MATLAB 119 PART II: Once More ... Microeconomics Chapter 8: General Equilibrium Models in GAMS 149 Game Theory Chapter 9: Cournot Duopoly in Mathematica (with Daniel Gaynor) 173 Chapter 10: Stackelberg Duopoly in Mathematica (with Daniel Gaynor) 189 Chapter 11: Genetic Algorithms and Evolutionary Games in MATLAB 201 Finance Chapter 12: Genetic Algorithms and Portfolio Models in MATLAB 223 Macroeconomics Chapter 13: Macroeconomics in GAMS 247 Agent-Based Computational Economics Chapter 14: Agent-Based Model in MATLAB 267 Environmental Economics Chapter 15: Global Warming in GAMS 291 Dynamic Optimization Chapter 16: Dynamic Optimization in MATLAB 309 PART III: Special Topic:tochastic Control Stochastic Control Chapter 17: Stochastic Control in Duali 339 Chapter 18: Rational Expectations Macro in Duali 361 APPENDIXES A. Running GAMS 389 B. Running Mathematica 391 C. Running the Solver in Excel 393 D. Ordered Sets in GAMS 394 E. Linearization and State-Space Representation of Hall and Taylor's Model 396 F. Introduction to Nonlinear Optimization Solvers 403 G. Linear Programming Solvers 407 H. The Stacking Method in GAMS 411 I. Running MATLAB 413 J. Obtaining the Steady State of the Growth Model 414 References 417 Index 425

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Book SynopsisExplains how to execute computationally intensive analysis on very large data sets. This book shows readers how to determine some of the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.Trade Review"This book presents an original, cheap and powerful solution to the problem of analysis of large data sets... The book is devoted mainly to the practitioner of Statistics, but is also useful to mathematicians, computer scientists, researchers and students in the biology, economics and social sciences."--Radu Trimbitas, StudiaUBBTable of ContentsPreface xi Chapter 1. Statistics in the modern day 1 PART I COMPUTING 15 Chapter 2. C 17 2.1 Lines 18 2.2 Variables and their declarations 28 2.3 Functions 34 2.4 The debugger 43 2.5 Compiling and running 48 2.6 Pointers 53 2.7 Arrays and other pointer tricks 59 2.8 Strings 65 2.9 *Errors 69 Chapter 3. Databases 74 3.1 Basic queries 76 3.2 *Doing more with queries 80 3.3 Joins and subqueries 87 3.4 On database design 94 3.5 Folding queries into C code 98 3.6 Maddening details 103 3.7 Some examples 108 Chapter 4. Matrices and models 113 4.1 The GSL's matrices and vectors 114 4.2 apo_da t120 4.3 Shunting data 123 4.4 Linear algebra 129 4.5 Numbers 135 4.6 *gsl_matrixand gsl_ve torinternals 140 4.7 Models 143 Chapter 5. Graphics 157 5.1 plot 160 5.2 *Some common settings 163 5.3 From arrays to plots 166 5.4 A sampling of special plots 171 5.5 Animation 177 5.6 On producing good plots 180 5.7 *Graphs--nodes and flowcharts 182 5.8 Printing and LATEX 185 Chapter 6. *More coding tools 189 6.1 Function pointers 190 6.2 Data structures 193 6.3 Parameters 203 6.4 *Syntactic sugar 210 6.5 More tools 214 PART II STATISTICS 217 Chapter 7. Distributions for description 219 7.1 Moments 219 7.2 Sample distributions 235 7.3 Using the sample distributions 252 7.4 Non-parametric description 261 Chapter 8. Linear projections 264 8.1 *Principal component analysis 265 8.2 OLS and friends 270 8.3 Discrete variables 280 8.4 Multilevel modeling 288 Chapter 9. Hypothesis testing with the CLT 295 9.1 The Central Limit Theorem 297 9.2 Meet the Gaussian family 301 9.3 Testing a hypothesis 307 9.4 ANOVA 312 9.5 Regression 315 9.6 Goodness of fit 319 Chapter 10. Maximum likelihood estimation 325 10.1 Log likelihood and friends 326 10.2 Description: Maximum likelihood estimators 337 10.3 Missing data 345 10.4 Testing with likelihoods 348 Chapter 11. Monte Carlo 356 11.1 Random number generation 357 11.2 Description: Finding statistics for a distribution 364 11.3 Inference: Finding statistics for a parameter 367 11.4 Drawing a distribution 371 11.5 Non-parametric testing 375 Appendix A: Environments and makefiles 381 A.1 Environment variables 381 A.2 Paths 385 A.3 Make 387 Appendix B: Text processing 392 B.1 Shell scripts 393 B.2 Some tools for scripting 398 B.3 Regular expressions 403 B.4 Adding and deleting 413 B.5 More examples 415 Appendix C: Glossary 419 Bibliography 435 Index 443

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Book SynopsisOffers a modern and authoritative treatment of the mathematical processes that underlie performance modeling. This book looks at the fundamentals of probability theory, from the basic concepts of set-based probability, through probability distributions, to bounds, limit theorems, and the laws of large numbers.Trade Review"The book represents a valuable text for courses in statistics and stochastic processes, so it is strongly recommended to libraries."--Hassan S. Bakouch, Journal of Applied StatisticsTable of ContentsPreface and Acknowledgments xv PART I PROBABILITY 1 Chapter 1: Probability 3 1.1 Trials, Sample Spaces, and Events 3 1.2 Probability Axioms and Probability Space 9 1.3 Conditional Probability 12 1.4 Independent Events 15 1.5 Law of Total Probability 18 1.6 Bayes' Rule 20 1.7 Exercises 21 Chapter 2: Combinatorics--The Art of Counting 25 2.1 Permutations 25 2.2 Permutations with Replacements 26 2.3 Permutations without Replacement 27 2.4 Combinations without Replacement 29 2.5 Combinations with Replacements 31 2.6 Bernoulli (Independent) Trials 33 2.7 Exercises 36 Chapter 3: Random Variables and Distribution Functions 40 3.1 Discrete and Continuous Random Variables 40 3.2 The Probability Mass Function for a Discrete Random Variable 43 3.3 The Cumulative Distribution Function 46 3.4 The Probability Density Function for a Continuous Random Variable 51 3.5 Functions of a Random Variable 53 3.6 Conditioned Random Variables 58 3.7 Exercises 60 Chapter 4: Joint and Conditional Distributions 64 4.1 Joint Distributions 64 4.2 Joint Cumulative Distribution Functions 64 4.3 Joint Probability Mass Functions 68 4.4 Joint Probability Density Functions 71 4.5 Conditional Distributions 77 4.6 Convolutions and the Sum of Two Random Variables 80 4.7 Exercises 82 Chapter 5: Expectations and More 87 5.1 Definitions 87 5.2 Expectation of Functions and Joint Random Variables 92 5.3 Probability Generating Functions for Discrete Random Variables 100 5.4 Moment Generating Functions 103 5.5 Maxima and Minima of Independent Random Variables 108 5.6 Exercises 110 Chapter 6: Discrete Distribution Functions 115 6.1 The Discrete Uniform Distribution 115 6.2 The Bernoulli Distribution 116 6.3 The Binomial Distribution 117 6.4 Geometric and Negative Binomial Distributions 120 6.5 The Poisson Distribution 124 6.6 The Hypergeometric Distribution 127 6.7 The Multinomial Distribution 128 6.8 Exercises 130 Chapter 7: Continuous Distribution Functions 134 7.1 The Uniform Distribution 134 7.2 The Exponential Distribution 136 7.3 The Normal or Gaussian Distribution 141 7.4 The Gamma Distribution 145 7.5 Reliability Modeling and the Weibull Distribution 149 7.6 Phase-Type Distributions 155 7.6.1 The Erlang-2 Distribution 155 7.6.2 The Erlang-r Distribution 158 7.6.3 The Hypoexponential Distribution 162 7.6.4 The Hyperexponential Distribution 164 7.6.5 The Coxian Distribution 166 7.6.6 General Phase-Type Distributions 168 7.6.7 Fitting Phase-Type Distributions to Means and Variances 171 7.7 Exercises 176 Chapter 8: Bounds and Limit Theorems 180 8.1 The Markov Inequality 180 8.2 The Chebychev Inequality 181 8.3 The Chernoff Bound 182 8.4 The Laws of Large Numbers 182 8.5 The Central Limit Theorem 184 8.6 Exercises 187 PART II MARKOV CHAINS 191 Chapter 9: Discrete- and Continuous-Time Markov Chains 193 9.1 Stochastic Processes and Markov Chains 193 9.2 Discrete-Time Markov Chains: Definitions 195 9.3 The Chapman-Kolmogorov Equations 202 9.4 Classification of States 206 9.5 Irreducibility 214 9.6 The Potential, Fundamental, and Reachability Matrices 218 9.6.1 Potential and Fundamental Matrices and Mean Time to Absorption 219 9.6.2 The Reachability Matrix and Absorption Probabilities 223 9.7 Random Walk Problems 228 9.8 Probability Distributions 235 9.9 Reversibility 248 9.10 Continuous-Time Markov Chains 253 9.10.1 Transition Probabilities and Transition Rates 254 9.10.2 The Chapman-Kolmogorov Equations 257 9.10.3 The Embedded Markov Chain and State Properties 259 9.10.4 Probability Distributions 262 9.10.5 Reversibility 265 9.11 Semi-Markov Processes 265 9.12 Renewal Processes 267 9.13 Exercises 275 Chapter 10: Numerical Solution of Markov Chains 285 10.1 Introduction 285 10.1.1 Setting the Stage 285 10.1.2 Stochastic Matrices 287 10.1.3 The Effect of Discretization 289 10.2 Direct Methods for Stationary Distributions 290 10.2.1 Iterative versus Direct Solution Methods 290 10.2.2 Gaussian Elimination and LU Factorizations 291 10.3 Basic Iterative Methods for Stationary Distributions 301 10.3.1 The Power Method 301 10.3.2 The Iterative Methods of Jacobi and Gauss-Seidel 305 10.3.3 The Method of Successive Overrelaxation 311 10.3.4 Data Structures for Large Sparse Matrices 313 10.3.5 Initial Approximations, Normalization, and Convergence 316 10.4 Block Iterative Methods 319 10.5 Decomposition and Aggregation Methods 324 10.6 The Matrix Geometric/Analytic Methods for Structured Markov Chains 332 10.6.1 The Quasi-Birth-Death Case 333 10.6.2 Block Lower Hessenberg Markov Chains 340 10.6.3 Block Upper Hessenberg Markov Chains 345 10.7 Transient Distributions 354 10.7.1 Matrix Scaling and Powering Methods for Small State Spaces 357 10.7.2 The Uniformization Method for Large State Spaces 361 10.7.3 Ordinary Differential Equation Solvers 365 10.8 Exercises 375 PART III QUEUEING MODELS 383 Chapter 11: Elementary Queueing Theory 385 11.1 Introduction and Basic Definitions 385 11.1.1 Arrivals and Service 386 11.1.2 Scheduling Disciplines 395 11.1.3 Kendall's Notation 396 11.1.4 Graphical Representations of Queues 397 11.1.5 Performance Measures--Measures of Effectiveness 398 11.1.6 Little's Law 400 11.2 Birth-Death Processes: The M/M/1 Queue 402 11.2.1 Description and Steady-State Solution 402 11.2.2 Performance Measures 406 11.2.3 Transient Behavior 412 11.3 General Birth-Death Processes 413 11.3.1 Derivation of the State Equations 413 11.3.2 Steady-State Solution 415 11.4 Multiserver Systems 419 11.4.1 The M/M/c Queue 419 11.4.2 The M/M/?Queue 425 11.5 Finite-Capacity Systems--The M/M/1/K Queue 425 11.6 Multiserver, Finite-Capacity Systems--The M/M/c/K Queue 432 11.7 Finite-Source Systems--The M/M/c//M Queue 434 11.8 State-Dependent Service 437 11.9 Exercises 438 Chapter 12: Queues with Phase-Type Laws: Neuts' Matrix-Geometric Method 444 12.1 The Erlang-r Service Model--The M/Er/1 Queue 444 12.2 The Erlang-r Arrival Model--The Er/M/1 Queue 450 12.3 The M/H2/1 and H2/M/1 Queues 454 12.4 Automating the Analysis of Single-Server Phase-Type Queues 458 12.5 The H2/E3/1 Queue and General Ph/Ph/1 Queues 460 12.6 Stability Results for Ph/Ph/1 Queues 466 12.7 Performance Measures for Ph/Ph/1 Queues 468 12.8 Matlab code for Ph/Ph/1 Queues 469 12.9 Exercises 471 Chapter 13: The z-Transform Approach to Solving Markovian Queues 475 13.1 The z-Transform 475 13.2 The Inversion Process 478 13.3 Solving Markovian Queues using z-Transforms 484 13.3.1 The z-Transform Procedure 484 13.3.2 The M/M/1 Queue Solved using z-Transforms 484 13.3.3 The M/M/1 Queue with Arrivals in Pairs 486 13.3.4 The M/Er/1 Queue Solved using z-Transforms 488 13.3.5 The Er/M/1 Queue Solved using z-Transforms 496 13.3.6 Bulk Queueing Systems 503 13.4 Exercises 506 Chapter 14: The M/G/1 and G/M/1 Queues 509 14.1 Introduction to the M/G/1 Queue 509 14.2 Solution via an Embedded Markov Chain 510 14.3 Performance Measures for the M/G/1 Queue 515 14.3.1 The Pollaczek-Khintchine Mean Value Formula 515 14.3.2 The Pollaczek-Khintchine Transform Equations 518 14.4 The M/G/1 Residual Time: Remaining Service Time 523 14.5 The M/G/1 Busy Period 526 14.6 Priority Scheduling 531 14.6.1 M/M/1: Priority Queue with Two Customer Classes 531 14.6.2 M/G/1: Nonpreemptive Priority Scheduling 533 14.6.3 M/G/1: Preempt-Resume Priority Scheduling 536 14.6.4 A Conservation Law and SPTF Scheduling 538 14.7 The M/G/1/K Queue 542 14.8 The G/M/1 Queue 546 14.9 The G/M/1/K Queue 551 14.10 Exercises 553 Chapter 15: Queueing Networks 559 15.1 Introduction 559 15.1.1 Basic Definitions 559 15.1.2 The Departure Process--Burke's Theorem 560 15.1.3 Two M/M/1 Queues in Tandem 562 15.2 Open Queueing Networks 563 15.2.1 Feedforward Networks 563 15.2.2 Jackson Networks 563 15.2.3 Performance Measures for Jackson Networks 567 15.3 Closed Queueing Networks 568 15.3.1 Definitions 568 15.3.2 Computation of the Normalization Constant: Buzen's Algorithm 570 15.3.3 Performance Measures 577 15.4 Mean Value Analysis for Closed Queueing Networks 582 15.5 The Flow-Equivalent Server Method 591 15.6 Multiclass Queueing Networks and the BCMP Theorem 594 15.6.1 Product-Form Queueing Networks 595 15.6.2 The BCMP Theorem for Open, Closed, and Mixed Queueing Networks 598 15.7 Java Code 602 15.8 Exercises 607 PART IV SIMULATION 611 Chapter 16: Some Probabilistic and Deterministic Applications of Random Numbers 613 16.1 Simulating Basic Probability Scenarios 613 16.2 Simulating Conditional Probabilities, Means, and Variances 618 16.3 The Computation of Definite Integrals 620 16.4 Exercises 623 Chapter 17: Uniformly Distributed "Random" Numbers 625 17.1 Linear Recurrence Methods 626 17.2 Validating Sequences of Random Numbers 630 17.2.1 The Chi-Square "Goodness-of-Fit" Test 630 17.2.2 The Kolmogorov-Smirnov Test 633 17.2.3 "Run" Tests 634 17.2.4 The "Gap" Test 640 17.2.5 The "Poker" Test 641 17.2.6 Statistical Test Suites 644 17.3 Exercises 644 Chapter 18: Nonuniformly Distributed "Random" Numbers 647 18.1 The Inverse Transformation Method 647 18.1.1 The Continuous Uniform Distribution 649 18.1.2 "Wedge-Shaped" Density Functions 649 18.1.3 "Triangular" Density Functions 650 18.1.4 The Exponential Distribution 652 18.1.5 The Bernoulli Distribution 653 18.1.6 An Arbitrary Discrete Distribution 653 18.2 Discrete Random Variates by Mimicry 654 18.2.1 The Binomial Distribution 654 18.2.2 The Geometric Distribution 655 18.2.3 The Poisson Distribution 656 18.3 The Accept-Reject Method 657 18.3.1 The Lognormal Distribution 660 18.4 The Composition Method 662 18.4.1 The Erlang-r Distribution 662 18.4.2 The Hyperexponential Distribution 663 18.4.3 Partitioning of the Density Function 664 18.5 Normally Distributed Random Numbers 670 18.5.1 Normal Variates via the Central Limit Theorem 670 18.5.2 Normal Variates via Accept-Reject and Exponential Bounding Function 670 18.5.3 Normal Variates via Polar Coordinates 672 18.5.4 Normal Variates via Partitioning of the Density Function 673 18.6 The Ziggurat Method 673 18.7 Exercises 676 Chapter 19: Implementing Discrete-Event Simulations 680 19.1 The Structure of a Simulation Model 680 19.2 Some Common Simulation Examples 682 19.2.1 Simulating the M/M/1 Queue and Some Extensions 682 19.2.2 Simulating Closed Networks of Queues 686 19.2.3 The Machine Repairman Problem 689 19.2.4 Simulating an Inventory Problem 692 19.3 Programming Projects 695 Chapter 20: Simulation Measurements and Accuracy 697 20.1 Sampling 697 20.1.1 Point Estimators 698 20.1.2 Interval Estimators/Confidence Intervals 704 20.2 Simulation and the Independence Criteria 707 20.3 Variance Reduction Methods 711 20.3.1 Antithetic Variables 711 20.3.2 Control Variables 713 20.4 Exercises 716 Appendix A: The Greek Alphabet 719 Appendix B: Elements of Linear Algebra 721 B.1 Vectors and Matrices 721 B.2 Arithmetic on Matrices 721 B.3 Vector and Matrix Norms 723 B.4 Vector Spaces 724 B.5 Determinants 726 B.6 Systems of Linear Equations 728 B.6.1 Gaussian Elimination and LU Decompositions 730 B.7 Eigenvalues and Eigenvectors 734 B.8 Eigenproperties of Decomposable, Nearly Decomposable, and Cyclic Stochastic Matrices 738 B.8.1 Normal Form 738 B.8.2 Eigenvalues of Decomposable Stochastic Matrices 739 B.8.3 Eigenvectors of Decomposable Stochastic Matrices 741 B.8.4 Nearly Decomposable Stochastic Matrices 743 B.8.5 Cyclic Stochastic Matrices 744 Bibliography 745 Index 749

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Book SynopsisProvides readers with the skills they need to write computer codes that simulate convection, internal gravity waves, and magnetic field generation in the interiors and atmospheres of rotating planets and stars. This book describes how to create codes that simulate the internal dynamics of planets and stars.Trade Review"This book provides readers with the skills they need to write computer codes that simulate convection, internal gravity waves and magnetic field generation in the interiors and atmospheres of rotating planets and stars. It is very useful for readers having a basic understanding of classical physics, vector calculus, partial differential equations, and simple computer programming."--Claudia-Veronika Meister, Zentralblatt MATHTable of ContentsPreface xi PART I. THE FUNDAMENTALS 1 Chapter 1 A Model of Rayleigh-Benard Convection 3 1.1 Basic Theory 3 1.2 Boussinesq Equations 10 1.3 Model Description 13 Supplemental Reading 15 Exercises 15 Chapter 2 Numerical Method 17 2.1 Vorticity-Streamfunction Formulation 17 2.2 Horizontal Spectral Decomposition 19 2.3 Vertical Finite-Difference Method 21 2.4 Time Integration Scheme 22 2.5 Poisson Solver 24 Supplemental Reading 25 Exercises 25 Chapter 3 Linear Stability Analysis 27 3.1 Linear Equations 27 3.2 Linear Code 29 3.3 Critical Rayleigh Number 30 3.4 Analytic Solutions 31 Supplemental Reading 34 Exercises 34 Computational Projects 34 Chapter 4 Nonlinear Finite-Amplitude Dynamics 35 4.1 Modifications to the Linear Model 35 4.2 A Galerkin Method 36 4.3 Nonlinear Code 38 4.4 Nonlinear Simulations 43 Supplemental Reading 48 Exercises 49 Computational Projects 49 Chapter 5 Postprocessing 51 5.1 Computing and Storing Results 51 5.2 Displaying Results 51 5.3 Analyzing Results 54 Supplemental Reading 57 Exercises 57 Computational Projects 57 Chapter 6 Internal Gravity Waves 59 6.1 Linear Dispersion Relation 59 6.2 Code Modifications and Simulations 62 6.3 Wave Energy Analysis 66 Supplemental Reading 66 Exercises 67 Computational Projects 67 Chapter 7 Double-Diffusive Convection 68 7.1 Salt-Fingering Instability 69 7.2 Semiconvection Instability 72 7.3 Oscillating Instabilities 74 7.4 Staircase Profiles 76 7.5 Double-Diffusive Nonlinear Simulations 79 Supplemental Reading 80 Exercises 80 Computational Projects 80 PART II. ADDITIONAL NUMERICAL METHODS 83 Chapter 8 Time Integration Schemes 85 8.1 Fourth-Order Runge-Kutta Scheme 85 8.2 Semi-Implicit Scheme 87 8.3 Predictor-Corrector Schemes 89 8.4 Infinite Prandtl Number: Mantle Convection 91 Supplemental Reading 92 Exercises 93 Computational Projects 93 Chapter 9 Spatial Discretizations 95 9.1 Nonuniform Grid 95 9.2 Coordinate Mapping 97 9.3 Fully Finite Difference 98 9.4 Fully Spectral: Chebyshev-Fourier 102 9.5 Parallel Processing 108 Supplemental Reading 112 Exercises 112 Computational Projects 112 Chapter 10 Boundaries and Geometries 115 10.1 Absorbing Top and Bottom Boundaries 115 10.2 Permeable Periodic Side Boundaries 117 10.3 2D Annulus Geometry 122 10.4 Spectral-Transform Method 130 10.5 3D and 2.5D Cartesian Box Geometry 133 10.6 3D and 2.5D Spherical-Shell Geometry 135 Supplemental Reading 162 Exercises 162 Computational Projects 164 PART III. ADDITIONAL PHYSICS 167 Chapter 11 Magnetic Field 169 11.1 Magnetohydrodynamics 170 11.2 Magnetoconvection with a Vertical Background Field 173 11.3 Linear Analyses: Magnetic 179 11.4 Nonlinear Simulations: Magnetic 182 11.5 Magnetoconvection with a Horizontal Background Field 184 11.6 Magnetoconvection with an Arbitrary Background Field 187 Supplemental Reading 189 Exercises 190 Computational Projects 191 Chapter 12 Density Stratification 193 12.1 Anelastic Approximation 194 12.2 Reference State: Polytropes 207 12.3 Numerical Method: Anelastic 214 12.4 Linear Analyses: Anelastic 219 12.5 Nonlinear Simulations: Anelastic 222 Supplemental Reading 227 Exercises 227 Computational Projects 228 Chapter 13 Rotation 229 13.1 Coriolis, Centrifugal, and Poincare Forces 229 13.2 2D Rotating Equatorial Box 233 13.3 2D Rotating Equatorial Annulus: Differential Rotation 241 13.4 2.5D Rotating Spherical Shell: Inertial Oscillations 247 13.5 3D Rotating Spherical Shell: Dynamo Benchmarks 259 13.6 3D Rotating Spherical Shell: Dynamo Simulations 264 13.7 Concluding Remarks 275 Supplemental Reading 277 Exercises 278 Computational Projects 279 Appendix A A Tridiagonal Matrix Solver 283 Appendix B Making Computer-Graphical Movies 284 Appendix C Legendre Functions and Gaussian Quadrature 288 Appendix D Parallel Processing: OpenMP 291 Appendix E Parallel Processing: MPI 292 Bibliography 295 Index 307

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Book SynopsisFocusing on fundamental aspects of numerical methods for stochastic computations, this book describes the class of numerical methods based on generalized polynomial chaos (gPC). It illustrates through examples Basic gPC methods, and includes polynomial approximation theory and probability theory.Trade Review"[A]s a newbie to this field, by reading this lively written text I was able to gain insight into this really interesting and challenging matter."--Peter Mathe, Mathematical ReviewsTable of ContentsPreface xi Chapter 1: Introduction 1 1.1 Stochastic Modeling and Uncertainty Quantification 1 1.1.1 Burgers' Equation: An Illustrative Example 1 1.1.2 Overview of Techniques 3 1.1.3 Burgers' Equation Revisited 4 1.2 Scope and Audience 5 1.3 A Short Review of the Literature 6 Chapter 2: Basic Concepts of Probability Theory 9 2.1 Random Variables 9 2.2 Probability and Distribution 10 2.2.1 Discrete Distribution 11 2.2.2 Continuous Distribution 12 2.2.3 Expectations and Moments 13 2.2.4 Moment-Generating Function 14 2.2.5 Random Number Generation 15 2.3 Random Vectors 16 2.4 Dependence and Conditional Expectation 18 2.5 Stochastic Processes 20 2.6 Modes of Convergence 22 2.7 Central Limit Theorem 23 Chapter 3: Survey of Orthogonal Polynomials and Approximation Theory 25 3.1 Orthogonal Polynomials 25 3.1.1 Orthogonality Relations 25 3.1.2 Three-Term Recurrence Relation 26 3.1.3 Hypergeometric Series and the Askey Scheme 27 3.1.4 Examples of Orthogonal Polynomials 28 3.2 Fundamental Results of Polynomial Approximation 30 3.3 Polynomial Projection 31 3.3.1 Orthogonal Projection 31 3.3.2 Spectral Convergence 33 3.3.3 Gibbs Phenomenon 35 3.4 Polynomial Interpolation 36 3.4.1 Existence 37 3.4.2 Interpolation Error 38 3.5 Zeros of Orthogonal Polynomials and Quadrature 39 3.6 Discrete Projection 41 Chapter 4: Formulation of Stochastic Systems 44 4.1 Input Parameterization: Random Parameters 44 4.1.1 Gaussian Parameters 45 4.1.2 Non-Gaussian Parameters 46 4.2 Input Parameterization: Random Processes and Dimension Reduction 47 4.2.1 Karhunen-Loeve Expansion 47 4.2.2 Gaussian Processes 50 4.2.3 Non-Gaussian Processes 50 4.3 Formulation of Stochastic Systems 51 4.4 Traditional Numerical Methods 52 4.4.1 Monte Carlo Sampling 53 4.4.2 Moment Equation Approach 54 4.4.3 Perturbation Method 55 Chapter 5: Generalized Polynomial Chaos 57 5.1 Definition in Single Random Variables 57 5.1.1 Strong Approximation 58 5.1.2 Weak Approximation 60 5.2 Definition in Multiple Random Variables 64 5.3 Statistics 67 Chapter 6: Stochastic Galerkin Method 68 6.1 General Procedure 68 6.2 Ordinary Differential Equations 69 6.3 Hyperbolic Equations 71 6.4 Diffusion Equations 74 6.5 Nonlinear Problems 76 Chapter 7: Stochastic Collocation Method 78 7.1 Definition and General Procedure 78 7.2 Interpolation Approach 79 7.2.1 Tensor Product Collocation 81 7.2.2 Sparse Grid Collocation 82 7.3 Discrete Projection: Pseudospectral Approach 83 7.3.1 Structured Nodes: Tensor and Sparse Tensor Constructions 85 7.3.2 Nonstructured Nodes: Cubature 86 7.4 Discussion: Galerkin versus Collocation 87 Chapter 8: Miscellaneous Topics and Applications 89 8.1 Random Domain Problem 89 8.2 Bayesian Inverse Approach for Parameter Estimation 95 8.3 Data Assimilation by the Ensemble Kalman Filter 99 8.3.1 The Kalman Filter and the Ensemble Kalman Filter 100 8.3.2 Error Bound of the EnKF 101 8.3.3 Improved EnKF via gPC Methods 102 Appendix A: Some Important Orthogonal Polynomials in the Askey Scheme 105 A.1 Continuous Polynomials 106 A.2 Discrete Polynomials 108 Appendix B: The Truncated Gaussian Model G(a?, ?ss) 113 References 117 Index 127

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Book SynopsisTrade Review"This book is a clear introduction to experimentation with complex systems that will appeal to multiple audiences. . . . It will serve as an example of pedagogical clarity and skill for anyone responsible for teaching the physical sciences."---H. Van Dyke Parunak, Computing Reviews"There is a certain enthusiasm distilled by the author all through the book, transporting the reader on a journey of discovery of a chosen set of complex systems, from where diverse insights into complexity science can be grasped. . . . Natural Complexity constitutes an excellent introduction to some perspectives about complexity science that might be appealing to a broad range of readers."---Miguel A. F. Sanjuán, Contemporary PhysicsTable of ContentsPreface xiii 1. Introduction: What Is Complexity? 1 1.1 Complexity Is Not Simple 1 1.2 Randomness Is Not Complexity 4 1.3 Chaos Is Not Complexity 10 1.4 Open Dissipative Systems 13 1.5 Natural Complexity 16 1.6 About the Computer Programs Listed in This Book 18 1.7 Suggested Further Reading 20 2 Iterated Growth 23 2.1 Cellular Automata in One Spatial Dimension 23 2.2 Cellular Automata in Two Spatial Dimensions 31 2.3 A Zoo of 2-D Structures from Simple Rules 38 2.4 Agents, Ants, and Highways 41 2.5 Emergent Structures and Behaviors 46 2.6 Exercises and Further Computational Explorations 47 2.7 Further Reading 50 3 Aggregation 53 3.1 Diffusion-Limited Aggregation 53 3.2 Numerical Implementation 54 3.3 A Representative Simulation 58 3.4 A Zoo of Aggregates 60 3.5 Fractal Geometry 63 3.6 Self-Similarity and Scale Invariance 73 3.7 Exercises and Further Computational Explorations 76 3.8 Further Reading 78 4 Percolation 80 4.1 Percolation in One Dimension 80 4.2 Percolation in Two Dimensions 83 4.3 Cluster Sizes 85 4.4 Fractal Clusters 98 4.5 Is It Really a Power Law? 98 4.6 Criticality 100 4.7 Exercises and Further Computational Explorations 102 4.8 Further Reading 104 5 Sandpiles 106 5.1 Model Definition 106 5.2 Numerical Implementation 110 5.3 A Representative Simulation 112 5.4 Measuring Avalanches 119 5.5 Self-Organized Criticality 123 5.6 Exercises and Further Computational Explorations 127 5.7 Further Reading 129 6 Forest Fires 130 6.1 Model Definition 130 6.2 Numerical Implementation 131 6.3 A Representative Simulation 134 6.4 Model Behavior 137 6.5 Back to Criticality 147 6.6 The Pros and Cons of Wildfire Management 148 6.7 Exercises and Further Computational Explorations 149 6.8 Further Reading 152 7 Traffic Jams 154 7.1 Model Definition 154 7.2 Numerical Implementation 157 7.3 A Representative Simulation 157 7.4 Model Behavior 161 7.5 Traffic Jams as Avalanches 164 7.6 Car Traffic as a SOC System? 168 7.7 Exercises and Further Computational Explorations 170 7.8 Further Reading 172 8 Earthquakes 174 8.1 The Burridge-Knopoff Model 175 8.2 Numerical Implementation 182 8.3 A Representative Simulation 184 8.4 Model Behavior 189 8.5 Predicting Real Earthquakes 193 8.6 Exercises and Further Computational Explorations 194 8.7 Further Reading 196 9 Epidemics 198 9.1 Model Definition 198 9.2 Numerical Implementation 199 9.3 A Representative Simulation 202 9.4 Model Behavior 205 9.5 Epidemic Self-Organization 213 9.6 Small-World Networks 215 9.7 Exercises and Further Computational Explorations 220 9.8 Further Reading 222 10 Flocking 224 10.1 Model Definition 225 10.2 Numerical Implementation 228 10.3 A Behavioral Zoo 235 10.4 Segregation of Active and Passive Flockers 240 10.5 Why You Should Never Panic 242 10.6 Exercises and Further Computational Explorations 245 10.7 Further Reading 247 11 Pattern Formation 249 11.1 Excitable Systems 249 11.2 The Hodgepodge Machine 253 11.3 Numerical Implementation 260 11.4 Waves, Spirals, Spaghettis, and Cells 262 11.5 Spiraling Out 266 11.6 Spontaneous Pattern Formation 270 11.7 Exercises and Further Computational Explorations 272 11.8 Further Reading 273 12 Epilogue 275 12.1 A Hike on Slickrock 275 12.2 Johannes Kepler and the Unity of Nature 279 12.3 From Lichens to Solar Flares 285 12.4 Emergence and Natural Order 288 12.5 Into the Abyss: Your Turn 290 12.6 Further Reading 291 A. Basic Elements of the Python Programming Language 293 A.1 Code Structure 294 A.2 Variables and Arrays 297 A.3 Operators 299 A.4 Loop Constructs 300 A.5 Conditional Constructs 304 A.6 Input/Output and Graphics 305 A.7 Further Reading 306 B. Probability Density Functions 308 B.1 A Simple Example 308 B.2 Continuous PDFs 312 B.3 Some Mathematical Properties of Power-Law PDFs 313 B.4 Cumulative PDFs 314 B.5 PDFs with Logarithmic Bin Sizes 315 B.6 Better Fits to Power-Law PDFs 318 B.7 Further Reading 320 C Random Numbers and Walks 321 C.1 Random and Pseudo-Random Numbers 321 C.2 Uniform Random Deviates 323 C.3 Using Random Numbers for Probability Tests 324 C.4 Nonuniform Random Deviates 325 C.5 The Classical Random Walk 328 C.6 Random Walk and Diffusion 335 D Lattice Computation 338 D.1 Nearest-Neighbor Templates 339 D.2 Periodic Boundary Conditions 342 D.3 Random Walks on Lattices 345 Index 351

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Book SynopsisThis practical guide provides system administrators, DevSecOps engineers, and cloud architects with a concise yet comprehensive overview on how to use PL/SQL to develop resilient database solutions.

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Book SynopsisDeveloping a data curious organization will take advantage of the burgeoning data resources available as a result of increasing digitalization. With this book, author Carl Allchin shows today's business professionals how to become data empowered.

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Book SynopsisThe first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data?and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD)?providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing?also known as data mining?and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Trade Review“This book is a unique resource for practitioners and researchers in computer science, life science, and mathematics.” (Zentralblatt MATH, 1 June 2015) Table of ContentsPREFACE xiii CONTRIBUTORS xv SECTION I BIOLOGICAL DATA PREPROCESSING PART A: BIOLOGICAL DATA MANAGEMENT 1 GENOME AND TRANSCRIPTOME SEQUENCE DATABASES FOR DISCOVERY, STORAGE, AND REPRESENTATION OF ALTERNATIVE SPLICING EVENTS 5 Bahar Taneri and Terry Gaasterland 2 CLEANING, INTEGRATING, AND WAREHOUSING GENOMIC DATA FROM BIOMEDICAL RESOURCES 35 Fouzia Moussouni and Laure Berti-Equille 3 CLEANSING OF MASS SPECTROMETRY DATA FOR PROTEIN IDENTIFICATION AND QUANTIFICATION 59 Penghao Wang and Albert Y. Zomaya 4 FILTERING PROTEIN–PROTEIN INTERACTIONS BY INTEGRATION OF ONTOLOGY DATA 77 Young-Rae Cho PART B: BIOLOGICAL DATA MODELING 5 COMPLEXITY AND SYMMETRIES IN DNA SEQUENCES 95 Carlo Cattani 6 ONTOLOGY-DRIVEN FORMAL CONCEPTUAL DATA MODELING FOR BIOLOGICAL DATA ANALYSIS 129 Catharina Maria Keet 7 BIOLOGICAL DATA INTEGRATION USING NETWORK MODELS 155 Gaurav Kumar and Shoba Ranganathan 8 NETWORK MODELING OF STATISTICAL EPISTASIS 175 Ting Hu and Jason H. Moore 9 GRAPHICAL MODELS FOR PROTEIN FUNCTION AND STRUCTURE PREDICTION 191 Mingjie Tang, Kean Ming Tan, Xin Lu Tan, Lee Sael, Meghana Chitale, Juan Esquivel-Rodrýguez, and Daisuke Kihara PART C: BIOLOGICAL FEATURE EXTRACTION 10 ALGORITHMS AND DATA STRUCTURES FOR NEXT-GENERATION SEQUENCES 225 Francesco Vezzi, Giuseppe Lancia, and Alberto Policriti 11 ALGORITHMS FOR NEXT-GENERATION SEQUENCING DATA 251 Costas S. Iliopoulos and Solon P. Pissis 12 GENE REGULATORY NETWORK IDENTIFICATION WITH QUALITATIVE PROBABILISTIC NETWORKS 281 Zina M. Ibrahim, Alioune Ngom, and Ahmed Y. Tawfik PART D: BIOLOGICAL FEATURE SELECTION 13 COMPARING, RANKING, AND FILTERING MOTIFS WITH CHARACTER CLASSES: APPLICATION TO BIOLOGICAL SEQUENCES ANALYSIS 309 Matteo Comin and Davide Verzotto 14 STABILITY OF FEATURE SELECTION ALGORITHMS AND ENSEMBLE FEATURE SELECTION METHODS IN BIOINFORMATICS 333 Pengyi Yang, Bing B. Zhou, Jean Yee-Hwa Yang, and Albert Y. Zomaya 15 STATISTICAL SIGNIFICANCE ASSESSMENT FOR BIOLOGICAL FEATURE SELECTION: METHODS AND ISSUES 353 Juntao Li, Kwok Pui Choi, Yudi Pawitan, and Radha Krishna Murthy Karuturi 16 SURVEY OF NOVEL FEATURE SELECTION METHODS FOR CANCER CLASSIFICATION 379 Oleg Okun 17 INFORMATION-THEORETIC GENE SELECTION IN EXPRESSION DATA 399 Patrick E. Meyer and Gianluca Bontempi 18 FEATURE SELECTION AND CLASSIFICATION FOR GENE EXPRESSION DATA USING EVOLUTIONARY COMPUTATION 421 Haider Banka, Suresh Dara, and Mourad Elloumi SECTION II BIOLOGICAL DATA MINING PART E: REGRESSION ANALYSIS OF BIOLOGICAL DATA 19 BUILDING VALID REGRESSION MODELS FOR BIOLOGICAL DATA USING STATA AND R 445 Charles Lindsey and Simon J. Sheather 20 LOGISTIC REGRESSION IN GENOMEWIDE ASSOCIATION ANALYSIS 477 Wentian Li and Yaning Yang 21 SEMIPARAMETRIC REGRESSION METHODS IN LONGITUDINAL DATA: APPLICATIONS TO AIDS CLINICAL TRIAL DATA 501 Yehua Li PART F: BIOLOGICAL DATA CLUSTERING 22 THE THREE STEPS OF CLUSTERING IN THE POST-GENOMIC ERA 521 Raffaele Giancarlo, Giosu´e Lo Bosco, Luca Pinello, and Filippo Utro 23 CLUSTERING ALGORITHMS OF MICROARRAY DATA 557 Haifa Ben Saber, Mourad Elloumi, and Mohamed Nadif 24 SPREAD OF EVALUATION MEASURES FOR MICROARRAY CLUSTERING 569 Giulia Bruno and Alessandro Fiori 25 SURVEY ON BICLUSTERING OF GENE EXPRESSION DATA 591 Adelaide Valente Freitas, Wassim Ayadi, Mourad Elloumi, Jose Luis Oliveira, and Jin-Kao Hao 26 MULTIOBJECTIVE BICLUSTERING OF GENE EXPRESSION DATA WITH BIOINSPIRED ALGORITHMS 609 Khedidja Seridi, Laetitia Jourdan, and El-Ghazali Talbi 27 COCLUSTERING UNDER GENE ONTOLOGY DERIVED CONSTRAINTS FOR PATHWAY IDENTIFICATION 625 Alessia Visconti, Francesca Cordero, Dino Ienco, and Ruggero G. Pensa PART G: BIOLOGICAL DATA CLASSIFICATION 28 SURVEY ON FINGERPRINT CLASSIFICATION METHODS FOR BIOLOGICAL SEQUENCES 645 Bhaskar DasGupta and Lakshmi Kaligounder 29 MICROARRAY DATA ANALYSIS: FROM PREPARATION TO CLASSIFICATION 657 Luciano Cascione, Alfredo Ferro, Rosalba Giugno, Giuseppe Pigola, and Alfredo Pulvirenti 30 DIVERSIFIED CLASSIFIER FUSION TECHNIQUE FOR GENE EXPRESSION DATA 675 Sashikala Mishra, Kailash Shaw, and Debahuti Mishra 31 RNA CLASSIFICATION AND STRUCTURE PREDICTION: ALGORITHMS AND CASE STUDIES 685 Ling Zhong, Junilda Spirollari, Jason T. L. Wang, and Dongrong Wen 32 AB INITIO PROTEIN STRUCTURE PREDICTION: METHODS AND CHALLENGES 703 Jad Abbass, Jean-Christophe Nebel, and Nashat Mansour 33 OVERVIEW OF CLASSIFICATION METHODS TO SUPPORT HIV/AIDS CLINICAL DECISION MAKING 725 Khairul A. Kasmiran, Ali Al Mazari, Albert Y. Zomaya, and Roger J. Garsia PART H: ASSOCIATION RULES LEARNING FROM BIOLOGICAL DATA 34 MINING FREQUENT PATTERNS AND ASSOCIATION RULES FROM BIOLOGICAL DATA 737 Ioannis Kavakiotis, George Tzanis, and Ioannis Vlahavas 35 GALOIS CLOSURE BASED ASSOCIATION RULE MINING FROM BIOLOGICAL DATA 761 Kartick Chandra Mondal and Nicolas Pasquier 36 INFERENCE OF GENE REGULATORY NETWORKS BASED ON ASSOCIATION RULES 803 Cristian Andres Gallo, Jessica Andrea Carballido, and Ignacio Ponzoni PART I: TEXT MINING AND APPLICATION TO BIOLOGICAL DATA 37 CURRENT METHODOLOGIES FOR BIOMEDICAL NAMED ENTITY RECOGNITION 841 David Campos, Sergio Matos, and José Luýs Oliveira 38 AUTOMATED ANNOTATION OF SCIENTIFIC DOCUMENTS: INCREASING ACCESS TO BIOLOGICAL KNOWLEDGE 869 Evangelos Pafilis, Heiko Horn, and Nigel P. Brown 39 AUGMENTING BIOLOGICAL TEXT MINING WITH SYMBOLIC INFERENCE 901 Jong C. Park and Hee-Jin Lee 40 WEB CONTENT MINING FOR LEARNING GENERIC RELATIONS AND THEIR ASSOCIATIONS FROM TEXTUAL BIOLOGICAL DATA 919 Muhammad Abulaish and Jahiruddin 41 PROTEIN–PROTEIN RELATION EXTRACTION FROM BIOMEDICAL ABSTRACTS 943 Syed Toufeeq Ahmed, Hasan Davulcu, Sukru Tikves, Radhika Nair, and Chintan Patel PART J: HIGH-PERFORMANCE COMPUTING FOR BIOLOGICAL DATA MINING 42 ACCELERATING PAIRWISE ALIGNMENT ALGORITHMS BY USING GRAPHICS PROCESSOR UNITS 971 Mourad Elloumi, Mohamed Al Sayed Issa, and Ahmed Mokaddem 43 HIGH-PERFORMANCE COMPUTING IN HIGH-THROUGHPUT SEQUENCING 981 Kamer Kaya, Ayat Hatem, Hatice Gulcin Ozer, Kun Huang, and Umit V. Catalyurek 44 LARGE-SCALE CLUSTERING OF SHORT READS FOR METAGENOMICS ON GPUs 1003 Thuy Diem Nguyen, Bertil Schmidt, Zejun Zheng, and Chee Keong Kwoh SECTION III BIOLOGICAL DATA POSTPROCESSING PART K: BIOLOGICAL KNOWLEDGE INTEGRATION AND VISUALIZATION 45 INTEGRATION OF METABOLIC KNOWLEDGE FOR GENOME-SCALE METABOLIC RECONSTRUCTION 1027 Ali Masoudi-Nejad, Ali Salehzadeh-Yazdi, Shiva Akbari-Birgani, and Yazdan Asgari 46 INFERRING AND POSTPROCESSING HUGE PHYLOGENIES 1049 Stephen A. Smith and Alexandros Stamatakis 47 BIOLOGICAL KNOWLEDGE VISUALIZATION 1073 Rodrigo Santamarýa 48 VISUALIZATION OF BIOLOGICAL KNOWLEDGE BASED ON MULTIMODAL BIOLOGICAL DATA 1109 Hendrik Rohn and Falk Schreiber INDEX 1127

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Book SynopsisTime series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you effective ways to collect, persist, and access large-scale time series data for analysis.

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Book SynopsisToday, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you'll appreciate how data science is fundamentally altering our world, for better and for worse.

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Book SynopsisWith this practical guide, application designers, data scientists, and system administrators will explore ways to create visualizations that bring context and a sense of time to streaming text data. Author Anthony Aragues guides you through the concepts and tools you need to build visualizations for analyzing data as it arrives.

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Book SynopsisIn this practical book, Drill committers Charles Givre and Paul Rogers show analysts and data scientists how to query and analyze raw data using this powerful tool.

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Book SynopsisCompanies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite for mobile devicesLearn privacy-preserving machine learning techniques

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