Database design and theory Books
O'Reilly Media Deciphering Data Architectures
Book SynopsisData fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of each architecture to help data professionals understand its pros and cons.
£47.99
Atlantic Books The Hidden Half: The Unseen Forces That Influence
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
£10.44
O'Reilly Media Python Data Science Handbook
Book SynopsisWorking scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models.
£47.99
O'Reilly Media Mastering Kafka Streams and ksqlDB
Book SynopsisWith Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide shows data engineers how to use these tools to build highly scalable stream processing applications for moving, enriching, and transforming large amounts of data in real time.
£47.99
O'Reilly Media Redis Cookbook
Book SynopsisRedis is an open source, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. This book will provide developers with problem and solutions in our useful cookbook style. This is example driven ebook.
£13.59
St. Martin's Publishing Group Automating Inequality
Book SynopsisA powerful investigative look at data-based discrimination—and how technology affects civil and human rights and economic equity.
£18.99
O'Reilly Media Fundamentals of Data Visualization
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.
£47.99
Basic Books The Art of Statistics: How to Learn from Data
Book Synopsis
£17.59
Sage Publications Ltd Data Visualisation: A Handbook for Data Driven
Book SynopsisOne of the "six best books for data geeks" - Financial TimesWith over 200 images and extensive how-to and how-not-to examples, this new edition has everything students and scholars need to understand and create effective data visualisations. Combining ‘how to think’ instruction with a ‘how to produce’ mentality, this book takes readers step-by-step through analysing, designing, and curating information into useful, impactful tools of communication. With this book and its extensive collection of online support, readers can: Decide what visualisations work best for their data and their audience using the chart gallery See data visualisation in action and learn the tools to try it themselves Follow online checklists, tutorials, and exercises to build skills and confidence Get advice from the UK’s leading data visualisation trainer on everything from getting started to honing the craft. Trade ReviewMany books focus on using software to visualise data but fewer consider good design. Data Visualisation addresses this in an inherently practical way. This is a much needed book which recognises and clarifies the process of designing excellent graphics. -- Chris PlayfordWhat do we actually mean when we talk about Data Visualisation? How do we ′do it’? How can we ensure our research using Data Visualisation is effective and ethical? The answers are all here in this inspirational and invaluable guide. -- Thérèse A.G. LewisEverything I loved in the first edition of this valuable book has been incorporated into the second, including Kirk’s typology of data visualisation and masses of visual examples, but with more precise written arguments. This volume continues to fill the gap between overwhelming data and the visualisations that can facilitate understanding. -- Pamela WoolnerIn this second edition, Andy adds relevant content to what was already a fantastic framework for learning the fundamentals of data visualization, all with acute and critical eyes. As an educator, I find it an invaluable resource to students and myself alike. -- Isabel Meirelles While most works in this space focus on data journalism, scientific visualisations or other specialist audiences, this book targets decision makers and helps with everything from initial concepts and data preparation to editorial layouts. A refreshing angle and a compelling read. -- Elena SimperlTable of ContentsINTRODUCTION PART A FOUNDATIONS Chapter 1 Defining Data Visualisation Chapter 2 The Visualisation Design Process PART B THE HIDDEN THINKING Chapter 3 Formulating Your Brief Chapter 4 Working With Data Chapter 5 Establishing Your Editorial Thinking PART C DESIGN THINKING Chapter 6 Data Representation Chapter 7 Interactivity Chapter 8 Annotation Chapter 9 Colour Chapter 10 Composition EPILOGUE
£53.79
Technics Publications LLC Object-Role Modeling Fundamentals: A Practical
Book Synopsis
£32.79
AU Press Open Data Structures: An Introduction
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
£25.19
The Pragmatic Programmers Designing Data Governance from the Ground Up: Six
Book SynopsisBusinesses own more data than ever before, but it's of no value if you don't know how to use it. Data governance manages the people, processes, and strategy needed for deploying data projects to production. But doing it well is far from easy: Less than one fourth of business leaders say their organizations are data driven. In Designing Data Governance from the Ground Up, you'll build a cross-functional strategy to create roadmaps and stewardship for data-focused projects, embed data governance into your engineering practice, and put processes in place to monitor data after deployment. In the last decade, the amount of data people produced grew 3,000 percent. Most organizations lack the strategy to clean, collect, organize, and automate data for production-ready projects. Without effective data governance, most businesses will keep failing to gain value from the mountain of data that's available to them. There's a plethora of content intended to help DataOps and DevOps teams reach production, but 90 percent of projects trained with big data fail to reach production because they lack governance. This book shares six steps you can take to build a data governance strategy from scratch. You'll find a data framework, pull together a team of data stewards, build a data governance team, define your roadmap, weave data governance into your development process, and monitor your data in production Whether you're a chief data officer or individual contributor, this book will show you how to manage up, get the buy-in you need to build data governance, find the right colleagues to co-create data governance, and keep them engaged for the long haul.
£21.59
O'Reilly Media Data Modeling with Microsoft Power BI
Book Synopsis
£44.79
Elsevier Science & Technology Information Modeling and Relational Databases
Book SynopsisOffers an introduction to ORM (Object-Role Modeling). This book includes coverage of relational database concepts, and developments in SQL and XML. It features case studies and exercises, and the associated web site provides appendices, and links to ORM tools. This book is intended for systems analysts, information modelers, and programmers.Trade Review"This book is an excellent introduction to both information modeling in ORM and relational databases. The book is very clearly written in a step-by-step manner, and contains an abundance of well-chosen examples illuminating practice and theory in information modeling. I strongly recommend this book to anyone interested in conceptual modeling and databases." --Dr. Herman Balsters, Director of the Faculty of Industrial Engineering, University of Groningen, The NetherlandsTable of Contents1 Introduction 2 Information Levels and Frameworks 3 Conceptual Modeling: First Steps 4 Uniqueness Constraints 5 Mandatory Roles 6 Value, Set-Comparison and Subtype Constraints 7 Other Constraints and Final Checks 8 Entity Relationship Modeling 9 Data Modeling in UML 10 Advanced Modeling Issues 11 Relational Mapping 12 Data Manipulation with Relational Languages 13 Using Other Database Objects 14 Schema Transformations 15 Process and State Modeling 16 Other Modeling Aspects and Trends
£70.99
Elsevier Science Publishing Co Inc Modeling and Control of Drug Delivery Systems
Book SynopsisTable of Contents1. Hepatitis C Virus Epidemic Control Using a Nonlinear Adaptive Strategy 2. Integral Sliding Mode Control of Immune Response for Kidney Transplantation 3. Smart Drug Delivery Systems 4. Polymeric Transdermal Drug Delivery Systems 5. Stimuli-Responsive Polymers as Smart Drug Delivery Systems 6. Efficacy of Polymer-Based Wound Dressings in Chronic Wounds 7. Recent Progress of Transdermal Drug Delivery Systems for Biomedical Applications 8. Towards the Development of Delivery Systems of Bioactive Compounds With Eyes Set on Pharmacokinetics 9. Nanofiber: An Immerging Novel Drug Delivery System 10. Molecular Dynamics Simulations on Drug Delivery Systems 11. Nanoparticle Drug Delivery: An Advanced Approach for Highly Competent and Multifunctional Therapeutic Treatment 12. Targeted Drug Delivery: Advancements, Applications, and Challenges 13. Strategies-Based Intrathecal Targeted Drug Delivery System for Effective Therapy, Modeling, and Controlled Release Action 14. Biopolymer-Based Hydrogel Wound Dressing 15. Novel Controlled Release Pulmonary Drug Delivery Systems: Current updates and Challenges 16. Nanoparticle Formulations and Delivery Strategies for Sustained Drug Release in the Lungs 17. Current Perspectives on Mycosynthesis of Nanoparticles and Their Biomedical application 18. Solid Oral Controlled-Release Formulations 19. Advanced Solid Oral Controlled-Release Formulations 20. Mucoadhesive Polymers: Gateway to Innovative Drug Delivery
£74.96
Princeton University Press Individualbased Modeling and Ecology
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
£69.70
Princeton University Press Computational Economics
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
£110.40
Princeton University Press Dynamic Models in Biology
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
£73.60
Princeton University Press Modeling with Data
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
£73.60
Princeton University Press Probability Markov Chains Queues and Simulation
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
£100.30
Princeton University Press Introduction to Modeling Convection in Planets
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
£100.30
Princeton University Press Introduction to Modeling Convection in Planets
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
£56.00
Princeton University Press Numerical Methods for Stochastic Computations
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
£48.00
Princeton University Press Natural Complexity
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
£40.80
Princeton University Press Natural Complexity
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 Physics"[Clear], easy to read, and presents simple computational code to support and help explain each example/model."---Juan A. Bonachela, The Quarterly Review of BiologyTable 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
£76.00
Princeton University Press Text as Data
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
£67.20
Princeton University Press Text as Data
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
£32.30
Sanderson Press, LLC Hyper Changing the way you think about plan and execute business intelligence for real results real fast
£39.10
DecisionOne Press Agile Data Warehouse Design
£27.89
Taylor & Francis Ltd Practical Data Mining Techniques and Applications
Book SynopsisThis book focuses on how to use various data mining techniques to develop real-world applications. It offers practical applications with a clear understanding of data mining concepts. The book also has a special chapter on case studies of data mining in practice.
£50.34
O'Reilly Media Learning Google Analytics
Book SynopsisAuthor Mark Edmondson, Google Developer Expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations.
£39.74
O'Reilly Media Scaling Python with Dask
Book SynopsisWith this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn.
£47.99
O'Reilly Media Kafka Connect
Book SynopsisWith this practical guide, authors Mickael Maison and Kate Stanley show data engineers, site reliability engineers, and application developers how to build data pipelines between Kafka clusters and a variety of data sources and sinks.
£47.99
O'Reilly Media Resilient Oracle PlSQL
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.
£47.99
O'Reilly Media Delta Lake Up and Running
Book SynopsisWith the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data's quality. Delta Lake's open source format offers a robust lakehouse framework over platforms like Amazon S3, ADLS, and GCS.
£39.74
O'Reilly Media Data Curious
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.
£27.74
O'Reilly Media Automating Data Quality Monitoring at Scale
Book SynopsisIn this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately.
£39.74
Cambridge University Press Unsupervised Machine Learning for Clustering in
Book SynopsisIn the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.Table of Contents1. Introduction; 2. Setting the stage for clustering; 3. Agglomerative hierarchical clustering; 4. k-means clustering; 5. Gaussian mixture models; 6. Advanced methods; 7. Conclusion.
£16.15
John Wiley & Sons Inc Biological Knowledge Discovery Handbook
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
£146.66
John Wiley & Sons Inc Engineering Biostatistics
Book SynopsisProvides a one-stop resource for engineers learning biostatistics using MATLAB and WinBUGS Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing bio-oriented engineering fields while implementing software packages that are familiar to engineers. The book is heavily oriented to computation and hands-on approaches so readers understand each step of the programming. Another dimension of this book is in parallel coverage of both Bayesian and frequentist approaches to statistical inference. It avoids taking sides on the classical vs. Bayesian paradigms, and many examples in this book are solved using both methods. The results are then compared and commented upon. Readers have the choice of MATLAB for classical data analysis and WinBUGS/OpenBUGS for Bayesian data analysis. Every chapter starts with a box highlighting what is covered in that chapter and ends with exercises, a list of software scripts, datasets, and referenceTable of ContentsPreface v 1 Introduction 1 Chapter References 7 2 The Sample and Its Properties 9 2.1 Introduction 9 2.2 A MATLAB Session on Univariate Descriptive Statistics 10 2.3 Location Measures 12 2.4 Variability Measures 15 2.4.1 Ranks 24 2.5 Displaying Data 25 2.6 Multidimensional Samples: Fisher’s Iris Data and Body Fat Data 29 2.7 Multivariate Samples and Their Summaries 35 2.8 Principal Components of Data 40 2.9 Visualizing Multivariate Data 45 2.10 Observations as Time Series 49 2.11 About Data Types 52 2.12 Big Data Paradigm 53 2.13 Exercises 55 Chapter References 70 3 Probability, Conditional Probability, and Bayes’ Rule 73 3.1 Introduction 73 3.2 Events and Probability 74 3.3 Odds 85 3.4 Venn Diagrams 86 3.5 Counting Principles 88 3.6 Conditional Probability and Independence 92 3.6.1 Pairwise and Global Independence 97 3.7 Total Probability 97 3.8 Reassesing Probabilities: Bayes’ Rule 100 3.9 Bayesian Networks 105 3.10 Exercises 111 Chapter References 130 4 Sensitivity, Specificity, and Relatives 133 4.1 Introduction 133 4.2 Notation 134 4.2.1 Conditional Probability Notation 138 4.3 Combining Two or More Tests 141 4.4 ROC Curves 144 4.5 Exercises 149 Chapter References 157 5 Random Variables 159 5.1 Introduction 159 5.2 Discrete Random Variables 161 5.2.1 Jointly Distributed Discrete Random Variables 166 5.3 Some Standard Discrete Distributions 169 5.3.1 Discrete Uniform Distribution 169 5.3.2 Bernoulli and Binomial Distributions 170 5.3.3 Hypergeometric Distribution 174 5.3.4 Poisson Distribution 177 5.3.5 Geometric Distribution 180 5.3.6 Negative Binomial Distribution 183 5.3.7 Multinomial Distribution 184 5.3.8 Quantiles 186 5.4 Continuous Random Variables 187 5.4.1 Joint Distribution of Two Continuous Random Variables 192 5.4.2 Conditional Expectation 193 5.5 Some Standard Continuous Distributions 195 5.5.1 Uniform Distribution 196 5.5.2 Exponential Distribution 198 5.5.3 Normal Distribution 200 5.5.4 Gamma Distribution 201 5.5.5 Inverse Gamma Distribution 203 5.5.6 Beta Distribution 203 5.5.7 Double Exponential Distribution 205 5.5.8 Logistic Distribution 206 5.5.9 Weibull Distribution 207 5.5.10 Pareto Distribution 208 5.5.11 Dirichlet Distribution 209 5.6 Random Numbers and Probability Tables 210 5.7 Transformations of Random Variables 211 5.8 Mixtures 214 5.9 Markov Chains 215 5.10 Exercises 219 Chapter References 232 6 Normal Distribution 235 6.1 Introduction 235 6.2 Normal Distribution 236 6.2.1 Sigma Rules 240 6.2.2 Bivariate Normal Distribution 241 6.3 Examples with a Normal Distribution 243 6.4 Combining Normal Random Variables 246 6.5 Central Limit Theorem 249 6.6 Distributions Related to Normal 253 6.6.1 Chi-square Distribution 254 6.6.2 t-Distribution 258 6.6.3 Cauchy Distribution 259 6.6.4 F-Distribution 260 6.6.5 Noncentral χ2, t, and F Distributions 262 6.6.6 Lognormal Distribution 263 6.7 Delta Method and Variance Stabilizing Transformations 265 6.8 Exercises 268 Chapter References 274 7 Point and Interval Estimators 277 7.1 Introduction 277 7.2 Moment Matching and Maximum Likelihood Estimators 278 7.2.1 Unbiasedness and Consistency of Estimators 285 7.3 Estimation of a Mean, Variance, and Proportion 288 7.3.1 Point Estimation of Mean 288 7.3.2 Point Estimation of Variance 290 7.3.3 Point Estimation of Population Proportion 294 7.4 Confidence Intervals 295 7.4.1 Confidence Intervals for the Normal Mean 296 7.4.2 Confidence Interval for the Normal Variance 299 7.4.3 Confidence Intervals for the Population Proportion . . . 302 7.4.4 Confidence Intervals for Proportions When X = 0 306 7.4.5 Designing the Sample Size with Confidence Intervals 307 7.5 Prediction and Tolerance Intervals 309 7.6 Confidence Intervals for Quantiles 311 7.7 Confidence Intervals for the Poisson Rate 312 7.8 Exercises 315 Chapter References 328 8 Bayesian Approach to Inference 331 8.1 Introduction 331 8.2 Ingredients for Bayesian Inference 334 8.3 Conjugate Priors 338 8.4 Point Estimation 340 8.4.1 Normal-Inverse Gamma Conjugate Analysis 343 8.5 Prior Elicitation 345 8.6 Bayesian Computation and Use of WinBUGS 348 8.6.1 Zero Tricks in WinBUGS 351 8.7 Bayesian Interval Estimation: Credible Sets 353 8.8 Learning by Bayes’ Theorem 357 8.9 Bayesian Prediction 358 8.10 Consensus Means 362 8.11 Exercises 365 Chapter References 372 9 Testing Statistical Hypotheses 375 9.1 Introduction 375 9.2 Classical Testing Problem 377 9.2.1 Choice of Null Hypothesis 377 9.2.2 Test Statistic, Rejection Regions, Decisions, and Errors in Testing 379 9.2.3 Power of the Test 380 9.2.4 Fisherian Approach: p-Values 381 9.3 Bayesian Approach to Testing 382 9.3.1 Criticism and Calibration of p-Values 386 9.4 Testing the Normal Mean 388 9.4.1 z-Test 389 9.4.2 Power Analysis of a z-Test 389 9.4.3 Testing a Normal Mean When the Variance Is Not Known: t-Test 391 9.4.4 Power Analysis of t-Test 394 9.5 Testing Multivariate Mean: T-Square Test∗ 397 9.5.1 T-Square Test 397 9.5.2 Test for Symmetry 401 9.6 Testing the Normal Variances 402 9.7 Testing the Proportion 404 9.7.1 Exact Test for Population Proportions 406 9.7.2 Bayesian Test for Population Proportions 409 9.8 Multiplicity in Testing, Bonferroni Correction, and False Discovery Rate 412 9.9 Exercises 415 Chapter References 425 10 Two Samples 427 10.1 Introduction 427 10.2 Means and Variances in Two Independent Normal Populations 428 10.2.1 Confidence Interval for the Difference of Means 433 10.2.2 Power Analysis for Testing Two Means 434 10.2.3 More Complex Two-Sample Designs 438 10.2.4 A Bayesian Test for Two Normal Means 439 10.3 Testing the Equality of Normal Means When Samples Are Paired 443 10.3.1 Sample Size in Paired t-Test 448 10.3.2 Difference-in-Differences (DiD) Tests 449 10.4 Two Multivariate Normal Means 451 10.4.1 Confidence Intervals for Arbitrary Linear Combinations of Mean Differences 453 10.4.2 Profile Analysis With Two Independent Groups 454 10.4.3 Paired Multivariate Samples 456 10.5 Two Normal Variances 459 10.6 Comparing Two Proportions 463 10.6.1 The Sample Size 465 10.7 Risk Differences, Risk Ratios, and Odds Ratios 466 10.7.1 Risk Differences 466 10.7.2 Risk Ratio 467 10.7.3 Odds Ratios 469 10.7.4 Two Proportions from a Single Sample 473 10.8 Two Poisson Rates 476 10.9 Equivalence Tests 479 10.10 Exercises 483 Chapter References 500 11 ANOVA and Elements of Experimental Design 503 11.1 Introduction 503 11.2 One-Way ANOVA 504 11.2.1 ANOVA Table and Rationale for F-Test 506 11.2.2 Testing Assumption of Equal Population Variances . . . 509 11.2.3 The Null Hypothesis Is Rejected. What Next? 511 11.2.4 Bayesian Solution 516 11.2.5 Fixed- and Random-Effect ANOVA 518 11.3 Welch’s ANOVA 518 11.4 Two-Way ANOVA and Factorial Designs 521 11.4.1 Two-way ANOVA: One Observation Per Cell 527 11.5 Blocking 529 11.6 Repeated Measures Design 531 11.6.1 Sphericity Tests 534 11.7 Nested Designs 535 11.8 Power Analysis in ANOVA 539 11.9 Functional ANOVA 545 11.10 Analysis of Means (ANOM) 548 11.11 Gauge R&R ANOVA 550 11.12 Testing Equality of Several Proportions 556 11.13 Testing the Equality of Several Poisson Means 557 11.14 Exercises 559 Chapter References 582 12 Models for Tables 585 12.1 Introduction 586 12.2 Contingency Tables: Testing for Independence 586 12.2.1 Measuring Association in Contingency Tables 591 12.2.2 Power Analysis for Contingency Tables 593 12.2.3 Cohen’s Kappa 594 12.3 Three-Way Tables 596 12.4 Fisher’s Exact Test 600 12.5 Stratified Tables: Mantel–Haenszel Test 603 12.5.1 Testing Conditional Independence or Homogeneity . . . 604 12.5.2 Odds Ratio from Stratified Tables 607 12.6 Paired Tables: McNemar’s Test 608 12.7 Risk Differences, Risk Ratios, and Odds Ratios for Paired Tables 610 12.7.1 Risk Differences 610 12.7.2 Risk Ratios 611 12.7.3 Odds Ratios 612 12.7.4 Liddell’s Procedure 617 12.7.5 Garth Test 619 12.7.6 Stuart–Maxwell Test 620 12.7.7 Cochran’s Q Test∗ 626 12.8 Exercises 628 Chapter References 643 13 Correlation 647 13.1 Introduction 647 13.2 The Pearson Coefficient of Correlation 648 13.2.1 Inference About ρ 650 13.2.2 Bayesian Inference for Correlation Coefficients 663 13.3 Spearman’s Coefficient of Correlation 665 13.4 Kendall’s Tau 667 13.5 Cum hoc ergo propter hoc 670 13.6 Exercises 671 Chapter References 677 14 Regression 679 14.1 Introduction 679 14.2 Simple Linear Regression 680 14.2.1 Inference in Simple Linear Regression 688 14.3 Calibration 697 14.4 Testing the Equality of Two Slopes 699 14.5 Multiple Regression 702 14.5.1 Matrix Notation 703 14.5.2 Residual Analysis, Influential Observations, Multicollinearity, and Variable Selection 709 14.6 Sample Size in Regression 720 14.7 Linear Regression That Is Nonlinear in Predictors 720 14.8 Errors-In-Variables Linear Regression 723 14.9 Analysis of Covariance 724 14.9.1 Sample Size in ANCOVA 728 14.9.2 Bayesian Approach to ANCOVA 729 14.10 Exercises 731 Chapter References 748 15 Regression for Binary and Count Data 751 15.1 Introduction 751 15.2 Logistic Regression 752 15.2.1 Fitting Logistic Regression 753 15.2.2 Assessing the Logistic Regression Fit 758 15.2.3 Probit and Complementary Log-Log Links 769 15.3 Poisson Regression 773 15.4 Log-linear Models 779 15.5 Exercises 783 Chapter References 798 16 Inference for Censored Data and Survival Analysis 801 16.1 Introduction 801 16.2 Definitions 802 16.3 Inference with Censored Observations 807 16.3.1 Parametric Approach 807 16.3.2 Nonparametric Approach: Kaplan–Meier or Product–Limit Estimator 809 16.3.3 Comparing Survival Curves 815 16.4 The Cox Proportional Hazards Model 818 16.5 Bayesian Approach 822 16.5.1 Survival Analysis in WinBUGS 823 16.6 Exercises 829 Chapter References 835 17 Goodness of Fit Tests 837 17.1 Introduction 837 17.2 Probability Plots 838 17.2.1 Q–Q Plots 838 17.2.2 P–P Plots 841 17.2.3 Poissonness Plots 842 17.3 Pearson’s Chi-Square Test 843 17.4 Kolmogorov–Smirnov Tests 852 17.4.1 Kolmogorov’s Test 852 17.4.2 Smirnov’s Test to Compare Two Distributions 854 17.5 Cramér-von Mises and Watson’s Tests 858 17.5.1 Rosenblatt’s Test 860 17.6 Moran’s Test 862 17.7 Departures from Normality 863 17.7.1 Ellimination of Unknown Parameters by Transformations 866 17.8 Exercises 867 Chapter References 876 18 Distribution-Free Methods 879 18.1 Introduction 879 18.2 Sign Test 880 18.3 Wilcoxon Signed-Rank Test 884 18.4 Wilcoxon Sum Rank Test and Mann–Whitney Test 887 18.5 Kruskal–Wallis Test 890 18.6 Friedman’s Test 894 18.7 Resampling Methods 898 18.7.1 The Jackknife 898 18.7.2 Bootstrap 901 18.7.3 Bootstrap Versions of Some Popular Tests 908 18.7.4 Randomization and Permutation Tests 916 18.7.5 Discussion 919 18.8 Exercises 919 Chapter References 929 19 Bayesian Inference Using Gibbs Sampling – BUGS Project 931 19.1 Introduction 931 19.2 Step-by-Step Session 932 19.3 Built-in Functions and Common Distributions in WinBUGS 937 19.4 MATBUGS: A MATLAB Interface to WinBUGS 938 19.5 Exercises 942 Chapter References 943 Index 945
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Book SynopsisIan Gwilt is Professor of Design at the University of South Australia.Trade ReviewA valuable counterpoint to the popular idea that data visualization is beautiful, this book provides a thoughtful and pragmatic position on material and experiential manifestations of data. It contains an array of perspectives on the subject, including the history of data’s material manifestations and the challenges of achieving human-centred design with increasingly complex socio-technical problems. -- Peter A. Hall, Reader in Graphic Design, UAL Camberwell, Chelsea and Wimbledon, UKThis is a fascinating anthology of fresh thinking on how we can understand our world through data. Materialist, sensory and phenomenological approaches to knowledge — long practiced in the Arts — are now having increasing impact on other disciplines. This book provides numerous examples and ideas on how materializing information can lead to more nuanced understandings and heightened engagement with data. Covering theory, practice and methodologies, this is an expansive and unique collection on the materialization of digital information -- Jon McCormack, SensiLab Director, Monash University, AustraliaTable of ContentsForeword, Karel van der Waarde (Graphic Design Consultant, Belgium) Introduction, Ian Gwilt (University of South Australia) Part One: Theories 1. Data Objects Thinking With Your Hands, Adrien Segal (California College of the Arts, USA) 2. Shifting Data between the Material and the Virtual is Not an Immaterial Matter, Dew Harrison (University of Wolverhampton, UK) 3. Data as Environment: Physicalization Strategies for Communicating Environmental Data, Dietmar Offenhuber (Northeastern University, USA) and Laura Perovich (MIT, USA) 4. Designing Explanations of Data-based Interactions in Socio-Technical Systems, Aaron Fry (Parsons School of Design, USA) 5. Moving Data: Visualizing Human and Nonhuman Movement Artistically, Michele Barker and Anna Munster (University of New South Wales, Australia) Part Two: Practices 6. Uncanny Landscapes: Experiential Encounters with Ecological Data, Zoë Sadokierski, Monica Monin and Andrew Burrell (University of Technology Sydney, Australia) 7. Exploring Digital-material Hybridity in the Postdigital Museum, Daniela Petrelli and Nick Dulake (Sheffield Hallam University, UK) 8. Socio-material Translations of Data and Value(s), Bettina Nissen (University of Edinburgh, UK) 9. Personal Data Manifestation: A Tangible Poetics of Data, Giles Lane (Proboscis, UK) and George Roussos (Birkbeck, University of London, UK) 10. Data and Emotion: The Climate Change Object, Karin von Ompteda (OCAD, Canada) Part Three: Techniques 11. Hybrid Data Constructs: Interacting with Biomedical Data in Augmented Spaces, Daniel F. Keefe, Bridger Herman, Jung Who Nam, Daniel Orban and Seth Johnson (University of Minnesota, USA) 12. Sonic Data Physicalization, Stephen Barrass (University of Canberra, Australia) 13. Making with Climate Data: Materiality, Metaphor and Engagement, Mitchell Whitelaw and Geoff Hinchcliffe (Australian National University) 14. Waterfalls as a Form of AI-based Feedback for Creativity Support, Georgi V. Georgiev and Yazan Barhoush (University of Oulu, Finland) 15. Data as Action: Constructing Dynamic Data Physicalizations, Jason Alexander (University of Bath, UK) Part Four: Trajectories 16. Making Data: The Next Generation, Ian Gwilt and Aaron Davis (University of South Australia)
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Machine Learning
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