Information architecture Books

Book SynopsisWhy does one smoker die of lung cancer but another live to 100? The answer is 'The Hidden Half' - those random, unknowable variables that mess up our attempts to comprehend the world.We humans are very clever creatures - but we're idiots about how clever we really are. In this entertaining and ingenious book, Blastland reveals how in our quest to make the world more understandable, we lose sight of how unexplainable it often is. The result - from GDP figures to medicine - is that experts know a lot less than they think. Filled with compelling stories from economics, genetics, business, and science, The Hidden Half is a warning that an explanation which works in one arena may not work in another. Entertaining and provocative, it will change how you view the world.Trade ReviewHighly original and challenging... Once you have read this book, you can't unread it. * Daniel Finkelstein, The Times *Fascinating and provocative. Blastland is one of the most original thinkers around. * Tim Harford - Financial Times columnist and author of The Undercover Economist *Elegantly written and mind-expanding, The Hidden Half will enthrall you with its storytelling while educating you with its scientific depth. * Daniel H. Pink - bestselling author of Drive *Brilliant. Blastland provides an explanation of the need for humility in the face of the inevitable limits to knowledge and our all-too-human temptation to tell stories about the world that go far beyond what the evidence will support. * Diane Coyle - Bennett Professor of Public Policy, Cambridge University *Fascinating... As John Wooden said, it's what you learn after you know it all that counts. * Andrew Gelman - author of Rich State Poor State Red State Blue State *Excellent. Blastland makes a compelling case that God is fond of playing dice with the cosmos-and the list of unpredictable things keeps growing, not shrinking. * Phillip Tetlock - bestselling author of Superforecasting *Beautifully written and often very funny. Anyone making decision that matter should enjoy this book and profit from its lessons. * Dame Frances Cairncross - Chair, Executive Committee of the Institute for Fiscal Studies *Thought-provoking. * UnHerd *Table of Contents0: Prologue 1: Bill is not Ben 2: I am not constant 3: Here is not there, now is not then 4: One path is not enough 5: The principle isn't practical 6: Big is not small 7: Big is not clear 8: The ignorant chicken 9: What to do 10: Postscript

Read more less

Book Synopsis

Read more less

Book Synopsis"This book is a comprehensive roadmap to the most crucial fix for today's broken Internet." - Brian Behlendorf, GM for Blockchain, Healthcare and Identity at the Linux Foundation In a world of changing privacy regulations, identity theft, and online anonymity, identity is a precious and complex concept. Self-Sovereign Identity (SSI) is a set of technologies that move control of digital identity from third party “identity providers”directly to individuals, and it promises to be one of the most important trendsfor the coming decades. Now in Self-Sovereign Identity, privacy and personal data experts Drummond Reed and Alex Preukschat lay out a roadmap for a futureof personal sovereignty powered by the Blockchain and cryptography. Cutting through the technical jargon with dozens of practical use cases from experts across all major industries, it presents a clear and compelling argument for why SSI is a paradigm shift, and shows how you can be ready to be prepared forit. about the technology Trust onthe internet is at an all-time low. Large corporations and institutions control our personal data because we've never had a simple, safe, strong way to prove who we are online. Self-sovereign identity (SSI) changes all that. about the book In Self-Sovereign Identity: Decentralized digital identity and verifiable credentials, you'll learn how SSI empowers us to receive digitally-signed credentials, store them in private wallets, and securely prove our online identities. It combines a clear, jargon-free introduction to this blockchain-inspired paradigm shift with interesting essays written by its leading practitioners. Whether for property transfer, ebanking, frictionless travel, or personalized services, the SSI model for digital trust will reshape our collective future. what's inside · The architecture of SSI software and services · The technical, legal, and governance concepts behind SSI · How SSI affects global business industry-by-industry · Emerging standards for SSI about the reader For technology and business readers. No prior SSI, cryptography, or blockchain experience required. aboutthe author Drummond Reed is the Chief Trust Officer at Evernym, a technology leader in SSI. Alex Preukschat is the co-founder of SSIMeetup.org and AlianzaBlockchain.org. Trade Review“This book is a comprehensive roadmap to the most crucial fix for today's broken Internet.” Brian Behlendorf, GM for Blockchain, Healthcare and Identity at the Linux Foundation “If trusted relationships over the Internet are important to youor your business, this book is for you.” John Jordan, Executive Director,Trust over IP Foundation “Decentralized identity represents not only a wide range of trust-enabling technologies, but also a paradigm shift in our increasingly digital-first world.” Rouven Heck, Executive Director, Decentralized Identity Foundation

Read more less

Book SynopsisThis practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures.

Read more less

Book Synopsis

Read more less

Book SynopsisOffered as an introduction to the field of data structures andalgorithms, Open Data Structures covers the implementation andanalysis of data structures for sequences (lists), queues, priorityqueues, unordered dictionaries, ordered dictionaries, and graphs.Focusing on a mathematically rigorous approach that is fast, practical,and efficient, Morin clearly and briskly presents instruction alongwith source code. Analyzed and implemented in Java, the data structures presented inthe book include stacks, queues, deques, and lists implemented asarrays and linked-lists; space-efficient implementations of lists; skiplists; hash tables and hash codes; binary search trees includingtreaps, scapegoat trees, and red-black trees; integer searchingstructures including binary tries, x-fast tries, and y-fast tries;heaps, including implicit binary heaps and randomized meldable heaps;and graphs, including adjacency matrix and adjacency listrepresentations; and B-trees. A modern treatment of an essential computer science topic, OpenData Structures is a measured balance between classical topics andstate-of-the art structures that will serve the needs of allundergraduate students or self-directed learners.Table of ContentsAcknowledgments- xi Why This Book?- xiii 1. Introduction- 1 1.1 The Need for Efficiency- 2 1.2 Interfaces- 4 1.3 Mathematical Background- 9 1.4 The Model of Computation- 18 1.5 Correctness, Time Complexity, and Space Complexity- 19 1.6 Code Samples- 22 1.7 List of Data Structures- 22 1.8 Discussion and Exercises- 26 2. Array-Based Lists- 29 2.1 ArrayStack: Fast Stack Operations Using an Array- 30 2.2 FastArrayStack: An Optimized ArrayStack- 35 2.3 ArrayQueue: An Array-Based Queue- 36 2.4 ArrayDeque: Fast Deque Operations Using an Array- 40 2.5 DualArrayDeque: Building a Deque from Two Stacks- 43 2.6 RootishArrayStack: A Space-Efficient Array Stack- 49 2.7 Discussion and Exercises- 59 3. Linked Lists- 63 3.1 SLList: A Singly-Linked List- 63 3.2 DLList: A Doubly-Linked List- 67 3.3 SEList: A Space-Efficient Linked List- 71 3.4 Discussion and Exercises- 82 4. Skiplists- 87 4.1 The Basic Structure- 87 4.2 SkiplistSSet: An Efficient Sset- 90 4.3 SkiplistList: An Efficient Random-Access List- 93 4.4 Analysis of Skiplists- 98 4.5 Discussion and Exercises- 102 5. Hash Tables- 107 5.1 ChainedHashTable: Hashing with Chaining- 107 5.2 LinearHashTable: Linear Probing- 114 5.3 Hash Codes- 122 5.4 Discussion and Exercises- 128 6. Binary Trees- 133 6.1 BinaryTree: A Basic Binary Tree- 135 6.2 BinarySearchTree: An Unbalanced Binary Search Tree- 140 6.3 Discussion and Exercises- 147 7. Random Binary Search Trees- 153 7.1 Random Binary Search Trees- 153 7.2 Treap: A Randomized Binary Search Tree- 159 7.3 Discussion and Exercises- 168 8. Scapegoat Trees- 173 8.1 ScapegoatTree: A Binary Search Tree with Partial Rebuilding-173 8.2 Discussion and Exercises- 181 9. Red-Black Trees- 185 9.1 2-4 Trees- 186 9.2 RedBlackTree: A Simulated 2-4 Tree- 190 9.3 Summary- 205 9.4 Discussion and Exercises- 206 10. Heaps- 211 10.1 BinaryHeap: An Implicit Binary Tree- 211 10.2 MeldableHeap: A Randomized Meldable Heap- 217 10.3 Discussion and Exercises- 222 11. Sorting Algorithms- 225 11.1 Comparison-Based Sorting- 226 11.2 Counting Sort and Radix Sort- 238 11.3 Discussion and Exercises- 243 12. Graphs- 247 12.1 AdjacencyMatrix: Representing a Graph by a Matrix- 249 12.2 AdjacencyLists: A Graph as a Collection of Lists- 252 12.3 Graph Traversal- 256 12.4 Discussion and Exercises- 261 13. Data Structures for Integers- 265 13.1 BinaryTrie: A digital search tree- 266 13.2 XFastTrie: Searching in Doubly-Logarithmic Time- 272 13.3 YFastTrie: A Doubly-Logarithmic Time SSet- 275 13.4 Discussion and Exercises- 280 14. External Memory Searching- 283 14.1 The Block Store- 285 14.2 B-Trees- 285 14.3 Discussion and Exercises- 304 Bibliography- 309 Index- 317

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

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

Read more less

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

Read more less

Book SynopsisFrom controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology. Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians. Linked to a Web site with computer-lab materials and exercises, Dynamic Models in Biology is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.Trade Review"What is remarkable about Dynamic Models in Biology is that it truly speaks to students of biological sciences. It puts biology first, and then tries to explain how mathematical tools can explain biological phenomena. Nothing else I've seen does this anywhere near as well. The authors have combined their experience to produce and excellent textbook."--Bill Satzer, MAA Reviews "This is a great book and I expect that it will play an important role in the teaching of mathematical biology and the development of the next generation of mathematical biologists for many years to come."--Marc Mangel, SIAM Review "Dynamic Models in Biology stands apart from existing textbooks in mathematical biology largely because of its interdisciplinary approach and its hands-on, project-oriented case studies and computer laboratories. In an effort to explore biology in more detail, the authors bravely chose a style that differs from the classical biomath texts ... whose focus is more on formal mathematics."--Lewi Stone, BioScience "The book begins with a stellar overview of the purpose of modeling, contrasting statistical with dynamical models, and theoretical with practical models both clearly and even-handedly...[E]ngaging the full breadth and depth of this book could be an education for both instructors and students alike."--Frederick R. Adler, Mathematical Biosciences "[S]tudents from both biology and mathematics can gain much from this book. Dynamic Models in Biology would be appropriate for use in a semester or two-quarter course; however, with judicious selection of topics, it can be used in a quarter. My students included undergraduates in biology with knowledge only of calculus, undergraduates in mathematics, and graduate students and academic staff in biology, all enrolled on a ten-week course... Overall, Dynamic Models in Biology fills an important niche in the biological modeling canon. It occupies a place on my shelf next to Edelstein-Keshet (1988) and Murray (1989), and like them, will become a well-thumbed reference."--Carole L. Hom, Environmental ConservationTable of ContentsList of Figures ix List of Tables xiv Preface xvi Chapter 1: What Are Dynamic Models? 1 1.1 Descriptive versus Mechanistic Models 2 1.2 Chinook Salmon 4 1.3 Bathtub Models 6 1.4 Many Bathtubs: Compartment Models 7 1.4.1 Enzyme Kinetics 8 1.4.2 The Modeling Process 11 1.4.3 Pharmacokinetic Models 13 1.5 Physics Models: Running and Hopping 16 1.6 Optimization Models 20 1.7 Why Bother? 21 1.8 Theoretical versus Practical Models 24 1.9 What's Next? 26 1.10 References 28 Chapter 2: Matrix Models and Structured Population Dynamics 31 2.1 The Population Balance Law 32 2.2 Age-Structured Models 33 2.2.1 The Leslie Matrix 34 2.2.2 Warning: Prebreeding versus Postbreeding Models 37 2.3 Matrix Models Based on Stage Classes 38 2.4 Matrices and Matrix Operations 42 2.4.1 Review of Matrix Operations 43 2.4.2 Solution of the Matrix Model 44 2.5 Eigenvalues and a Second Solution of the Model 44 2.5.1 Left Eigenvectors 48 2.6 Some Applications of Matrix Models 49 2.6.1 Why Do We Age? 49 2.6.2 Elasticity Analysis and Conservation Biology 52 2.6.3 How Much Should We Trust These Models? 58 2.7 Generalizing the Matrix Model 59 2.7.1 Stochastic Matrix Models 59 2.7.2 Density-Dependent Matrix Models 61 2.7.3 Continuous Size Distributions 63 2.8 Summary and Conclusions 66 2.9 Appendix 67 2.9.1 Existence and Number of Eigenvalues 67 2.9.2 Reproductive Value 67 2.10 References 68 Chapter 3: Membrane Channels and Action Potentials 71 3.1 Membrane Currents 72 3.1.1 Channel Gating and Conformational States 74 3.2 Markov Chains 77 3.2.1 Coin Tossing 78 3.2.2 Markov Chains 82 3.2.3 The Neuromuscular Junction 86 3.3 Voltage-Gated Channels 90 3.4 Membranes as Electrical Circuits 92 3.4.1 Reversal Potential 94 3.4.2 Action Potentials 95 3.5 Summary 103 3.6 Appendix: The Central Limit Theorem 104 3.7 References 106 Chapter 4: Cellular Dynamics: Pathways of Gene Expression 107 4.1 Biological Background 108 4.2 A Gene Network That Acts as a Clock 110 4.2.1 Formulating a Model 111 4.2.2 Model Predictions 113 4.3 Networks That Act as a Switch 119 4.4 Systems Biology 125 4.4.1 Complex versus Simple Models 129 4.5 Summary 131 4.6 References 132 Chapter 5: Dynamical Systems 135 5.1 Geometry of a Single Differential Equation 136 5.2 Mathematical Foundations: A Fundamental Theorem 138 5.3 Linearization and Linear Systems 141 5.3.1 Equilibrium Points 141 5.3.2 Linearization at Equilibria 142 5.3.3 Solving Linear Systems of Differential Equations 144 5.3.4 Invariant Manifolds 149 5.3.5 Periodic Orbits 150 5.4 Phase Planes 151 5.5 An Example: The Morris-Lecar Model 154 5.6 Bifurcations 160 5.7 Numerical Methods 175 5.8 Summary 181 5.9 References 181 Chapter 6: Differential Equation Models for Infectious Disease 183 6.1 Sir Ronald Ross and the Epidemic Curve 183 6.2 Rescaling the Model 187 6.3 Endemic Diseases and Oscillations 191 6.3.1 Analysis of the SIR Model with Births 193 6.3.2 Summing Up 197 6.4 Gonorrhea Dynamics and Control 200 6.4.1 A Simple Model and a Paradox 200 6.4.2 The Core Group 201 6.4.3 Implications for Control 203 6.5 Drug Resistance 206 6.6 Within-Host Dynamics of HIV 209 6.7 Conclusions 213 6.8 References 214 Chapter 7: Spatial Patterns in Biology 217 7.1 Reaction-Diffusion Models 218 7.2 The Turing Mechanism 223 7.3 Pattern Selection: Steady Patterns 226 7.4 Moving Patterns: Chemical Waves and Heartbeats 232 7.5 References 241 Chapter 8: Agent-Based and Other Computational Models for Complex Systems 243 8.1 Individual-Based Models in Ecology 245 8.1.1 Size-Dependent Predation 245 8.1.2 Swarm 247 8.1.3 Individual-Based Modeling of Extinction Risk 248 8.2 Artificial Life 252 8.2.1 Tierra 253 8.2.2 Microbes in Tierra 255 8.2.3 Avida 257 8.3 The Immune System and the Flu 259 8.4 What Can We Learn from Agent-Based Models? 260 8.5 Sensitivity Analysis 261 8.5.1 Correlation Methods 264 8.5.2 Variance Decomposition 266 8.6 Simplifying Computational Models 269 8.6.1 Separation of Time Scales 269 8.6.2 Simplifying Spatial Models 272 8.6.3 Improving the Mean Field Approximation 276 8.7 Conclusions 277 8.8 Appendix: Derivation of Pair Approximation 278 8.9 References 279 Chapter 9: Building Dynamic Models 283 9.1 Setting the Objective 284 9.2 Building an Initial Model 285 9.2.1 Conceptual Model and Diagram 286 9.3 Developing Equations for Process Rates 291 9.3.1 Linear Rates: When and Why? 291 9.3.2 Nonlinear Rates from "First Principles" 293 9.3.3 Nonlinear Rates from Data: Fitting Parametric Models 294 9.3.4 Nonlinear Rates from Data: Selecting a Parametric Model 298 9.4 Nonlinear Rates from Data: Nonparametric Models 302 9.4.1 Multivariate Rate Equations 304 9.5 Stochastic Models 306 9.5.1 Individual-Level Stochasticity 306 9.5.2 Parameter Drift and Exogenous Shocks 309 9.6 Fitting Rate Equations by Calibration 311 9.7 Three Commandments for Modelers 314 9.8 Evaluating a Model 315 9.8.1 Comparing Models 317 9.9 References 320 Index 323

Read more less

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

Read more less

Book SynopsisTrade Review"Among the metaverse of possible books on Text as Data that could have been published . . . I was pleased that my universe produced this one. I will assign this book as a critical part of my own course on content analysis for years to come, and it has already altered and improved the coherence of my own vocabulary and articulation for several critical choices underlying the process of turning text into data. . . . Highly recommend."---James Evans, Sociological Methods & Research

Read more less

Book SynopsisTrade Review"Among the metaverse of possible books on Text as Data that could have been published . . . I was pleased that my universe produced this one. I will assign this book as a critical part of my own course on content analysis for years to come, and it has already altered and improved the coherence of my own vocabulary and articulation for several critical choices underlying the process of turning text into data. . . . Highly recommend."---James Evans, Sociological Methods & Research

Read more less

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.

Read more less

Book Synopsis

Read more less

Book SynopsisData Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java. You'll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you'll find code examples you can use in your applications. Examine methods for obtaining, cleaning, and arranging data into its purest formUnderstand the matrix structure that your data should takeLearn basic concepts for testing the origin and validity of dataTransform your data into stable and usable numerical valuesUnderstand supervised and unsupe

Read more less

Book SynopsisMachine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles.

Read more less

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.

Read more less

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.

Read more less

Book SynopsisIn this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications.

Read more less

Book SynopsisData analysis expressions (DAX) is the formula language of Power Pivot. Learning the DAX language is key to empower Excel users so they can take advantage of these new Business Intelligence (BI) capabilities. This volume clearly explains the concepts of Power Pivot while at the same time offering hands-on practice to engage the reader and help new knowledge stick. This second edition has been updated for the Excel 2016 user interface while still providing a bridge for readers wanting to learn DAX in the Excel environment and then transfer their new DAX skills across to Power BI.

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisThis book adopts a holistic interpretation of information architecture, to offer libraries and information professionals a variety of methods, tools, and techniques that may be used when designing websites and information systems that support workflows and what people require when “managing information”. The editors argue that information architecture for libraries has largely been the study of content architecture and that, on the other hand, library assessment literature has dealt with performance measurement and change management strategies. There is a gap in the middle for information services, with little on the ways of looking at the process architecture of a library and information service and on methods for business process analysis. Information Systems: Process and practice aims to fill that gap with a combination of theory and supporting case studies written by an international line-up of contributors, including Sally Burford, Fernando Loizides, Catherine Burns and Adam Euerby. Case studies cover a wide variety of settings, from discrete resource discovery projects for academic and cultural institutions, through design for large organizational websites, the research evidence about user experience for semi-structured document design on websites, to the health sector with examples including patient support websites and clinical document management. This book: takes a holistic view and interpretation of Information architecture in the context of libraries across the sector, globally discusses research and methods that help libraries and information services work from strategic business objectives through the organisation of processes that support the information services offered, and information management functions supported opens a new area of research/investigation on the link between information behaviour research and information systems and architecture, supported by case studies and projects includes contributions from an international range of experts from diverse backgrounds uses introductory sections and chapter commentary from the editors to draw the discussions together. This will be essential reading for researchers in information science specifically in the areas of digital libraries, information architecture and information systems. It will also be useful for practitioners and students in these areas who want to know the different research issues and challenges and learn how they have been handled in course of various research projects in these areas.Trade Review'This book is not a simple ‘how to’ guide but really a set of pointers to launch the reader towards deeper research. The large number of references included with each chapter help to facilitate this, as even though research will inevitably move on, the citations will give good starting points into the literature for some years to come. Recommended for libraries that support LIS research and independent LIS researchers that wish to broaden the scope and application of their work.' -- Jon Knight * Journal of Librarianship and Information Science *Table of ContentsSeries editor’s foreword – Gobinda Chowdhury 1. Introduction – Christine Urquhart 2. Approaches to information architecture – Faten Hamad 3. ​Taxonomy testing for information architecture – Christine Urquhart 4. The enterprise website and its information structures – Sally Burford 5. Analysing activities, roles and processes – Christine Urquhart and Dina Tbaishat 6. Libraries and organization of library processes – history of operational research, and use of process modelling – Dina Tbaishat 7. Using RIVA process modelling to study book acquisition in academic libraries – Dina Tbaishat 8. Workflow analysis and process mapping in US academic libraries – Christine Urquhart 9. A Theoretical framework for designing and evaluating semi-structured document triage interfaces – Fernando Loizides and Aekaterini Mavri 10. Resource discovery case studies – Karen Colbron and Christine Urquhart 11. Increasing social connection through a Community of Practice inspired design – Catherine M. Burns and Adam Euerby 12. Methods for studying information provision, networking and communication in patient support groups – Cristina Vasilica and Paula Ormandy 13. Health information systems: clinical data capture and document architecture – Faten Hamad 14. Producing systematic reviews and getting evidence to the clinician – Faten Hamad

Read more less

Book SynopsisThe Future of Enriched, Linked, Open and Filtered Metadata is a comprehensive and accessible guide to creating accurate, consistent, complete, user-centred and quality metadata that supports the user tasks of finding, identifying, selecting, obtaining and exploring information resources. Based on the author’s many years of academic research and work as a cataloguing and metadata librarian, it shows readers how they can configure, create, enhance and enrich their metadata for print and digital resources. The book applies examples using MARC21, RDA, FRBR, BIBFRAME, subject headings and name authorities. It also uses screenshots from cutting edge library management systems, discovery interfaces and metadata tools. Coverage includes: definitions, discussions, and comparisons among MARC, FRBR, LRM, RDA, Linked Data and BIBFRAME standards and models discussion of the underlying principles and protocols of Linked Data vis-à-vis library metadata practical metadata configuration, creation, management, and cases employing cutting edge LMS, discovery interfaces, formats and tools discussion around why metadata needs to be enriched, linked, open and filtered to ensure the information resources described are discoverable and user friendly consideration of metadata as a growing and continuously enhancing, customer-focused and user-driven practice where the aim is to support users to find and retrieve relevant resources for their research and learning. This practical book uses simple and accessible language to make sense of the many existing and emerging metadata standards, models and approaches. It will be a valuable resource for anyone involved in metadata creation, management and utilisation as well as a reference for LIS students, especially those undertaking information organisation, cataloguing and metadata modules.Trade Review"This work is a tour de force...Thoroughly recommended." -- Ian McCallum * Journal of the Australian Library and Information Association *Table of Contents Introduction to metadata Metadata strategies and quality indicators Metadata use cases Contemporary metadata principles Enriched and linked metadata Open metadata Filtered Metadata FRBR, LRM and the Notion of Work Resource Description and Access (RDA) BIBFRAME: a new metadata framework Crowdsourcing and user-generated metadata

Read more less

Book SynopsisGain practical insights by exploiting data in your business to build advanced predictive modeling applications Key Features A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Master open source Python tools to build sophisticated predictive models Book Description Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: 1. Learning Predictive Analytics with Python 2. Mastering Predictive Analytics with Python What you will learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis Who this book is for This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move on from a conceptual understanding of advanced analytics and become an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you.Table of ContentsTable of Contents Module 1 Module 2

Read more less

Book SynopsisEnhance your data analysis and predictive modeling skills using popular Python toolsKey Features Cover all fundamental libraries for operation and manipulation of Python for data analysis Implement real-world datasets to perform predictive analytics with Python Access modern data analysis techniques and detailed code with scikit-learn and SciPy Book DescriptionPython is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations.Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations.In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques.By the end of this book, you will have hands-on experience performing data analysis with Python.What you will learn Explore important Python libraries and learn to install Anaconda distribution Understand the basics of NumPy Produce informative and useful visualizations for analyzing data Perform common statistical calculations Build predictive models and understand the principles of predictive analytics Who this book is forBecome a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this bookTable of ContentsTable of Contents The Anaconda Distribution and Jupyter Notebook Vectorizing Operations with Numpy Pandas: Everyone’s Favorite Data Analysis Library Visualization and Exploratory Data Analysis Statistical Computing with Python Introduction to Predictive Analytics Models

Read more less

Book SynopsisWith the continual development of professional industries in today's modernized world, certain technologies have become increasingly applicable. Cyber-physical systems, specifically, are a mechanism that has seen rapid implementation across numerous fields. This is a technology that is constantly evolving, so specialists need a handbook of research that keeps pace with the advancements and methodologies of these devices.Tools and Technologies for the Development of Cyber-Physical Systems is an essential reference source that discusses recent advancements of cyber-physical systems and its application within the health, information, and computer science industries. Featuring research on topics such as autonomous agents, power supply methods, and software assessment, this book is ideally designed for data scientists, technology developers, medical practitioners, computer engineers, researchers, academicians, and students seeking coverage on the development and various applications of cyber-physical systems.

Read more less

Book SynopsisStreamline your workflow, transform raw data into actionable insights, and use Alteryx Designer to shape, design, and visualize data Key Features Acquire the skills necessary to perform analytics operations like an expert Discover hidden trends and insights in your data from various sources to make accurate predictions Reduce the time and effort required to derive insights from your data Purchase of the print or Kindle book includes a free eBook in the PDF format Book DescriptionAlteryx allows you to create data manipulation and analytic workflows with a simple, easy-to-use, code-free UI, and perform fast-executing workflows, offering multiple ways to achieve the same results. The Alteryx Designer Cookbook is a comprehensive guide to maximizing your Alteryx skills and determining the best ways to perform data operations. This book's recipes will guide you through an analyst's complete journey, covering all aspects of the data life cycle. The first set of chapters will teach you how to read data from various sources to obtain reports and pass it through the required adjustment operations for analysis. After an explanation of the Alteryx platform components with a particular focus on Alteryx Designer, you’ll be taken on a tour of what and how you can accomplish by using this tool. Along the way, you’ll learn best practices and design patterns. The book also covers real-world examples to help you apply your understanding of the features in Alteryx to practical scenarios. By the end of this book, you’ll have enhanced your proficiency with Alteryx Designer and an improved ability to execute tasks within the tool efficiently.What you will learn Speed up the cleansing, data preparing, and shaping process Perform operations and transformations on the data to suit your needs Blend different types of data sources for analysis Pivot and un-pivot the data for easy manipulation Perform aggregations and calculations on the data Encapsulate reusable logic into macros Develop high-quality, data-driven reports to improve consistency Who this book is forThis book is for data analysts, data professionals, and business intelligence professionals seeking to harness the full potential of the tool. A basic understanding of Alteryx Designer and Alteryx terminology, including macros, apps, and workflows, is all you need to get started with this book.Table of ContentsTable of Contents Input data from files Working with databases Data Preparation Data Transformations Data Parsing Grouping Data Blending and Merging data Aggregations Dynamic Operations/ Tools Macros and Apps Downloads, APIs & Web Services Developer options Reporting with Alteryx Outputting Data

Read more less