{"title":"Database design and theory Books","description":"","products":[{"product_id":"python-data-science-handbook-9781098121228","title":"Python Data Science Handbook","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eWorking 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.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48738221654359,"sku":"9781098121228","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098121228.jpg?v=1723811833"},{"product_id":"data-visualisation-a-handbook-for-data-driven-design-9781526468925","title":"Data Visualisation: A Handbook for Data Driven","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003eOne of the \"six best books for data geeks\" - \u003cem\u003eFinancial Times\u003cbr\u003e\u003c\/em\u003e\u003c\/strong\u003e\u003cbr\u003eWith 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.\u003c\/p\u003e \u003cp\u003eCombining ‘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.\u003c\/p\u003e \u003cp\u003eWith this book and its extensive collection of online support, readers can:\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eDecide what visualisations work best for their data and their audience using the chart gallery\u003c\/li\u003e\n\u003cli\u003eSee data visualisation in action and learn the tools to try it themselves\u003c\/li\u003e\n\u003cli\u003eFollow online checklists, tutorials, and exercises to build skills and confidence\u003c\/li\u003e\n\u003cli\u003eGet advice from the UK’s leading data visualisation trainer on everything from getting started to honing the craft.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\u003cp\u003eMany books focus on using software to visualise data but fewer consider good design. \u003cem\u003eData Visualisation\u003c\/em\u003e addresses this in an inherently practical way. This is a much needed book which recognises and clarifies the process of designing excellent graphics.\u003c\/p\u003e -- Chris Playford\u003cbr\u003e\u003cp\u003eWhat 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.\u003c\/p\u003e -- Thérèse A.G. Lewis\u003cbr\u003e\u003cp\u003eEverything 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.\u003c\/p\u003e -- Pamela Woolner\u003cbr\u003e\u003cp\u003eIn 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.\u003c\/p\u003e -- Isabel Meirelles\u003cbr\u003e\u003cp\u003e \u003c\/p\u003e \u003cp\u003eWhile 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.\u003c\/p\u003e -- Elena Simperl\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eINTRODUCTION\t PART A FOUNDATIONS\t Chapter 1 Defining Data Visualisation\t Chapter 2 The Visualisation Design Process PART B THE HIDDEN THINKING\t Chapter 3 Formulating Your Brief\t Chapter 4 Working With Data\t Chapter 5 Establishing Your Editorial Thinking PART C DESIGN THINKING\t Chapter 6 Data Representation\t Chapter 7 Interactivity Chapter 8 Annotation Chapter 9 Colour Chapter 10 Composition EPILOGUE","brand":"Sage Publications Ltd","offers":[{"title":"Default Title","offer_id":48739943645527,"sku":"9781526468925","price":54.08,"currency_code":"GBP","in_stock":false}]},{"product_id":"kafka-in-action-9781617295232","title":"Kafka in Action","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003e\u003ci\u003eKafka in Action\u003c\/i\u003e is a practical, hands-on guide to building Kafka-based data pipelines. Filled with real-world use cases and scenarios, this book probes Kafka's most common use cases, ranging from simple logging through managing streaming data systems for message routing, analytics, and more. \u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eIn systems that handle big data, streaming data, or fast data, it's important to get your data pipelines right. Apache Kafka is a wicked-fast distributed streaming platform that operates as more than just a persistent log or a flexible message queue.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKey Features\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e·   Understanding Kafka's concepts\u003c\/p\u003e \u003cp\u003e·   Implementing Kafka as a message queue\u003c\/p\u003e \u003cp\u003e·   Setting up and executing basic ETL tasks\u003c\/p\u003e \u003cp\u003e·   Recording and consuming streaming data\u003c\/p\u003e \u003cp\u003e·   Working with Kafka producers and consumers from Java applications\u003c\/p\u003e \u003cp\u003e·   Using Kafka as part of a large data project team\u003c\/p\u003e \u003cp\u003e·   Performing Kafka developer and admin tasks\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eWritten for intermediate Java developers or data engineers. No prior knowledge of Kafka is required.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout the technology \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eApache Kafka is a distributed streaming platform for logging and streaming data between services or applications. With Kafka, it's easy to build applications that can act on or react to data streams as they flow through your system. Operational data monitoring, large scale message processing, website activity tracking, log aggregation, and more are all possible with Kafka.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eDylan Scott\u003c\/b\u003e is a software developer with over ten years of experience in Java and Perl. His experience includes implementing Kafka as a messaging system for a large data migration, and he uses Kafka in his work in the insurance industry.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48740643897687,"sku":"9781617295232","price":33.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617295232.jpg?v=1720055227"},{"product_id":"principles-of-data-management-facilitating-information-sharing-9781780175911","title":"Principles of Data Management: Facilitating","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003eData is a valuable corporate asset and its effective management is vital to an organisation’s success and survival. With this book you will learn to master the key principles of data management and use them to implement best practices in your organization.\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eThis professional guide covers all the key areas of data management, including database development and corporate data modelling. It is business-focused, providing the knowledge and techniques required to successfully implement the data management function.\u003c\/p\u003e \u003cp\u003eThis fully updated new edition provides new chapters on the most important data topics such as big data, artificial intelligence, linked data and concept systems. Principles of Data Management is fully aligned with syllabus for the BCS Professional Certificate in Data Management Essentials, making this the go-to text to unlocking the value of your data.\u003c\/p\u003e \u003cul\u003e\n\u003cli\u003eIdeal for business managers and all involved in the development of information systems as well as data management professionals\u003c\/li\u003e\n\u003cli\u003eComprehensive and descriptive view of data management\u003c\/li\u003e\n\u003cli\u003eSuitable for all levels, from beginners to advanced learners\u003c\/li\u003e\n\u003cli\u003eMust-read for anyone involved in the development of systems to manage data\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003eThis book is an excellent guide to understanding data management theory and techniques. It works at all levels: from beginner to advanced, and from reference source to the practicalities of implementation. I would highly recommend to anyone wanting to get to grips with data management, regardless of experience in the field. -- Ian Wallis, Managing Director, Data Strategists Ltd\u003cbr\u003eKeith has developed a broad and thorough understanding of all aspects of data management over many years, so is without doubt one of the authorities on data management. This updated book includes reference to a number of new techniques as well as refining existing guidance on data modelling and database structures. Keith clearly explains both the importance of planning and analysis of databases and repositories and an explanation of key techniques to achieve this. A ‘must buy’ for the bookshelf of any data management practitioner. -- Julian Schwarzenbach, Chair of the BCS Data Management Specialist Group\u003cbr\u003eThis book provides a comprehensive and descriptive view of data management within a database setting. This is a must read for anyone involved in the development of systems to manage data. This book is as useful as it is interesting. It covers everything you need to know about getting the most out of your data management processes and architecture. -- Ian Rush, Data \u0026amp; Process Advantage Ltd\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePart 1: Preliminaries Chapter 1 Data and the enterprise Chapter 2 Databases and their development Chapter 3 What is data management?  Part 2: Data Administration Chapter 4 Corporate data modelling Chapter 5 Data definition and naming Chapter 6 Metadata Chapter 7 Data quality Chapter 8 Data accessibility Chapter 9 Master data management  Part 3: Database and Repository Administration Chapter 10 Database administration Chapter 11 Repository administration  Part 4: The Data Management Environment Chapter 12 The use of packaged application software Chapter 13 Distributed data and databases Chapter 14 Business intelligence Chapter 15 Object orientation Chapter 16 Multimedia Chapter 17 Integrating data and web technology Chapter 18 Linked data Chapter 19 Concept systems Chapter 20 Big data and artificial intelligence  Appendices Appendix A Comparison of data modelling notations Appendix B Generic data models Appendix C HTML and XML Appendix D Techniques and skills for data management Appendix E Data strategy Appendix F International standards for data management Appendix G The BCS Data Management Essentials syllabus","brand":"BCS Learning \u0026 Development Limited","offers":[{"title":"Default Title","offer_id":48740983538007,"sku":"9781780175911","price":33.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781780175911.jpg?v=1720056212"},{"product_id":"cloud-data-architectures-demystified-gain-the-expertise-to-build-cloud-data-solutions-as-per-the-organizations-needs-9789355515810","title":"Cloud Data Architectures Demystified: Gain the","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"BPB Publications","offers":[{"title":"Default Title","offer_id":48743244398935,"sku":"9789355515810","price":29.92,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789355515810.jpg?v=1791483062"},{"product_id":"data-stewardship-9780128221327","title":"Data Stewardship","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"If you work in the field of data management, chance are you know of David Plotkin. He has worked in the field for decades and has written extensively on data, metadata, and related issues. With the second edition of his book, Plotkin has written the quintessential book on data stewardship. He defines data stewardship and how it fits together with data governance, delivering best practices, policies, and procedures that make sense. The book offers up practical advice on implementing data stewardship and defines the roles and responsibilities of the data steward. But he also devotes a chapter offering guidance on training for data stewards, and another chapter on the metrics that can be used to measure the performance of data stewards. And if your organization is new or just starting to implementing data governance and stewardship, then you absolutely must read Chapter 4, “Implementing Data Stewardship.” It will step you through the process of championing, communicating, and gaining support for data stewardship.\" --Data and Technology Today\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Data Stewardship and Data Governance: How They Fit Together2. Understanding the Types of Data Stewardship3. Data Stewardship Roles and Responsibilities4. Implementing Data Stewardship5. Training Business Data Stewards6. Practical Data Stewardship7. Important Roles of Data Stewards8. Measuring Data Stewardship Progress: The Metrics9. Rating Your Data Stewardship Maturity10. Big Data Stewardship and Data Lakes11. Governing and Stewarding your Data using Data Domains12. Summing It All UpAppendixA: Example Definition and DerivationB: Sample Training Plan Outlines","brand":"Elsevier Science Publishing Co Inc","offers":[{"title":"Default Title","offer_id":48864166871383,"sku":"9780128221327","price":44.64,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780128221327.jpg?v=1722270705"},{"product_id":"natural-complexity-9780691170350","title":"Natural Complexity","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"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.\"\u003cb\u003e---H. Van Dyke Parunak, \u003ci\u003eComputing Reviews\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\"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. . . . \u003ci\u003eNatural Complexity \u003c\/i\u003econstitutes an excellent introduction to some perspectives about complexity science that might be appealing to a broad range of readers.\"\u003cb\u003e---Miguel A. F. Sanjuán, \u003ci\u003eContemporary Physics\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface 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","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865534771543,"sku":"9780691170350","price":43.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691170350.jpg?v=1722274437"},{"product_id":"text-as-data-9780691207551","title":"Text as Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Among the metaverse of possible books on \u003ci\u003eText as Data\u003c\/i\u003e that could have been published . . . 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Highly recommend.\"\u003cb\u003e---James Evans, \u003ci\u003eSociological Methods \u0026amp; Research\u003c\/i\u003e\u003c\/b\u003e","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865547026775,"sku":"9780691207551","price":34.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691207551.jpg?v=1722274498"},{"product_id":"kafka-connect-9781098126537","title":"Kafka Connect","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eWith 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.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866331853143,"sku":"9781098126537","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098126537.jpg?v=1722278170"},{"product_id":"delta-lake-up-and-running-9781098139728","title":"Delta Lake Up and Running","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eWith the surge in big data and AI, organizations can rapidly create data products. 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This practical book provides a guided tour of each architecture to help data professionals understand its pros and cons.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48866333000023,"sku":"9781098150761","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098150761.jpg?v=1722278175"},{"product_id":"fundamentals-of-data-visualization-9781492031086","title":"Fundamentals of Data Visualization","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis 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.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867307061591,"sku":"9781492031086","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492031086.jpg?v=1722282710"},{"product_id":"mastering-kafka-streams-and-ksqldb-9781492062493","title":"Mastering Kafka Streams and ksqlDB","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eWith Kafka Streams and ksqlDB, building stream processing applications is easy and fun. 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With this handy pocket reference, author Ryan Sleeper (Innovative Tableau) shows you how to translate the vast amounts of data into useful information.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":48867310305623,"sku":"9781492093480","price":28.95,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781492093480.jpg?v=1722282725"},{"product_id":"the-nature-of-data-9781496217158","title":"The Nature of Data","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eBy synthesizing scholarly work at the intersection of political ecology, digital geography, and science and technology studies, The Nature of Data analyzes how new digital technologies affect environments and their control.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\"This book is a necessary piece to lay the groundwork for a political ecology of data and urge more research in this direction. . . . A welcome integration of digital social sciences, political ecology, critical GIS, and science and technology studies, and as such which will be of interest to scholars across these fields, but also to conservation practitioners. This collection of essays might also be useful as a methodological text for advanced graduate students.\"—Anne-Lise Boyer, H-Environment\u003cbr\u003e\"Thanks to insights from ecomedia studies, environmental humanists are increasingly studying how the environment becomes digital and the digital becomes environmental. \u003ci\u003eThe Nature of Data\u003c\/i\u003e ably contributes to this research.\"—Heather Houser, \u003ci\u003eISLE\u003c\/i\u003e\u003cbr\u003e“Data may not grow on trees, but it increasingly shapes how humans know, govern, and struggle over forests—and indeed, much of the nonhuman world. \u003ci\u003eThe Nature of Data\u003c\/i\u003e captures this moment empirically while advancing political ecology conceptually. 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Data, Colonialism, and the Transformation of Nature in the Pacific Northwest\u003cbr\u003e Anthony Levenda and Zbigniew Grabowski\u003cbr\u003e Part 2. Civic Science and Community-Driven Data\u003cbr\u003e 5. Environmental Sensing Infrastructures and Just Good Enough Data\u003cbr\u003e Jennifer Gabrys and Helen Pritchard\u003cbr\u003e 6. Collaborative Modeling as Sociotechnical Data Infrastructure in Rural Zimbabwe\u003cbr\u003e M. V. Eitzel, Jon Solera, K. B. Wilson, Abraham Mawere Ndlovu, Emmanuel Mhike Hove, Daniel Ndlovu, Abraham Changarara, Alice Ndlovu, Kleber Neves, Adnomore Chirindira, Oluwasola E. Omoju, Aaron C. Fisher, and André Veski\u003cbr\u003e 7. Citizen Scientists and Conservation in the Anthropocene: From Monitoring to Making Coral\u003cbr\u003e Irus Braverman\u003cbr\u003e 8. Data Infrastructures, Indigenous Knowledge, and Environmental Observing in the Arctic\u003cbr\u003e Noor Johnson, Colleen Strawhacker, and Peter Pulsifer\u003cbr\u003e 9. Digital Infrastructure and the Affective Nature of Value in Belize\u003cbr\u003e Patrick Gallagher\u003cbr\u003e 10. Infrastructuring Environmental Data Justice\u003cbr\u003e Dawn Walker, Eric Nost, Aaron Lemelin, Rebecca Lave, Lindsey Dillon, and Environmental Data and Governance Initiative (EDGI)\u003cbr\u003e Part 3. Governing Data, Infrastructuring Land and Resources\u003cbr\u003e 11. “A Poverty of Data”? Exporting the Digital Revolution to Farmers in the Global South\u003cbr\u003e Madeleine Fairbairn and Zenia Kish\u003cbr\u003e 12. Illicit Digital Environments: Monitoring and Surveilling Environmental Crime in Southeast Asia\u003cbr\u003e Hilary O. Faxon and Jenny Goldstein\u003cbr\u003e 13. Data Gaps: Penguin Science and Petrostate Formation in the Falkland Islands (Malvinas)\u003cbr\u003e James J. A. Blair\u003cbr\u003e 14. Data Structures, Indigenous Ontologies, and Hydropower in the U.S. Northwest\u003cbr\u003e Corrine Armistead\u003cbr\u003e 15. 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No experience with graph databases is required.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout the technology \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGraph databases store interconnected data in a more natural form, making them superior tools for representing data with rich relationships. Unlike in relational database management systems (RDBMS), where a more rigid view of data connections results in the loss of valuable insights, in graph databases, data connections are first priority.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eDave Bechberger\u003c\/b\u003e has extensive experience using graph databases as a product architect and a consultant. He’s spent his career leveraging cutting-edge technologies to build software in complex data domains such as bioinformatics, oil and gas, and supply chain management. 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Since 2014 he has focused on graph databases, especially in distributed or big data environments, and he regularly blogs and speaks at conferences about graph databases.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48886899769687,"sku":"9781617296376","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617296376.jpg?v=1722542086"},{"product_id":"data-modeling-master-class-training-manual-steve-hobermans-best-practices-approach-to-developing-a-competency-in-data-modeling-9781634620901","title":"Data Modeling Master Class Training Manual: Steve","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Technics Publications LLC","offers":[{"title":"Default Title","offer_id":48887162143063,"sku":"9781634620901","price":159.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781634620901.jpg?v=1722543304"},{"product_id":"soft-computing-developments-methods-applications-9781634851336","title":"Soft Computing: Developments, Methods \u0026","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Nova Science Publishers Inc","offers":[{"title":"Default Title","offer_id":48887238033751,"sku":"9781634851336","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781634851336.jpg?v=1722543636"},{"product_id":"data-modeling-master-class-training-manual-steve-hobermans-best-practices-approach-to-developing-a-competency-in-data-modeling-9781935504887","title":"Data Modeling Master Class Training Manual: Steve","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Technics Publications LLC","offers":[{"title":"Default Title","offer_id":48888777081175,"sku":"9781935504887","price":159.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781935504887.jpg?v=1722551091"},{"product_id":"data-fabric-architectures-web-driven-applications-9783111000824","title":"Data Fabric Architectures: Web-Driven","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eThe immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos. 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With this book, author Carl Allchin shows today's business professionals how to become data empowered.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49083820376407,"sku":"9781098143831","price":27.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098143831.jpg?v=1725550119"},{"product_id":"sql-nosql-databases-models-languages-consistency-options-and-architectures-for-big-data-management-9783658245481","title":"SQL \u0026 NoSQL Databases: Models, Languages,","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book offers a comprehensive introduction to relational (SQL) and non-relational (NoSQL) databases. The authors thoroughly review the current state of database tools and techniques, and examine coming innovations.\u003c\/p\u003e  \u003cp\u003eThe book opens with a broad look at data management, including an overview of information systems and databases, and an explanation of contemporary database types:\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eSQL and NoSQL databases, and their respective management systems\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eThe nature and uses of Big Data\u003cbr\u003e\n\u003c\/li\u003e\n\u003cli\u003eA high-level view of the organization of data management\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e  \u003cb\u003eData Modeling and Consistency \u003c\/b\u003e\u003cp\u003e\u003c\/p\u003e  \u003cp\u003eChapter-length treatment is afforded Data Modeling in both relational and graph databases, including enterprise-wide data architecture, and formulas for database design. 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The book benefits readers including students and practitioners working across the broad field of applied information technology.\u003c\/p\u003e\u003cp\u003eThis textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eData Management - Data Modeling - Database Languages - Ensuring Data Consistency - System Architecture - Post-Relational Databases - NoSQL Databases ","brand":"Springer Fachmedien Wiesbaden","offers":[{"title":"Default Title","offer_id":49084782707031,"sku":"9783658245481","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783658245481.jpg?v=1791482098"},{"product_id":"modeling-and-control-of-drug-delivery-systems-9780128211854","title":"Modeling and Control of Drug Delivery Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. 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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","brand":"Elsevier Science Publishing Co Inc","offers":[{"title":"Default Title","offer_id":49369126797655,"sku":"9780128211854","price":74.96,"currency_code":"GBP","in_stock":false}]},{"product_id":"data-science-with-java-9781491934111","title":"Data Science with Java","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eData Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. 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Examine methods for obtaining, cleaning, and arranging data into its purest formUnderstand the matrix structure that your data should takeLearn basic concepts for testing the origin and validity of dataTransform your data into stable and usable numerical valuesUnderstand supervised and unsupe","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49372169175383,"sku":"9781491934111","price":35.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781491934111.jpg?v=1791472362"},{"product_id":"handbook-of-image-processing-and-computer-vision-volume-1-from-energy-to-image-9783030381479","title":"Handbook of Image Processing and Computer Vision:","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eAcross three volumes, the Handbook of Image Processing and Computer Vision presents a comprehensive review of the full range of topics that comprise the field of computer vision, from the acquisition of signals and formation of images, to learning techniques for scene understanding. The authoritative insights presented within cover all aspects of the sensory subsystem required by an intelligent system to perceive the environment and act autonomously. Volume 1 (From Energy to Image) examines the formation, properties, and enhancement of a digital image.\u003c\/p\u003e\u003cp\u003eTopics and features:\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e•\tDescribes the fundamental processes in the field of artificial vision that enable the formation of digital images from light energy\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e•\tCovers light propagation, color perception, optical systems, and the analog-to-digital conversion of the signal\u003c\/p\u003e\u003cp\u003e•\tDiscusses the information recorded in a digital image, and the image processing algorithms that can improve the visual qualities of the image\u003c\/p\u003e\u003cp\u003e•\tReviews boundary extraction algorithms, key linear and geometric transformations, and techniques for image restoration\u003c\/p\u003e\u003cp\u003e•\tPresents a selection of different image segmentation algorithms, and of widely-used algorithms for the automatic detection of points of interest\u003c\/p\u003e\u003cp\u003e•\tExamines important algorithms for object recognition, texture analysis, 3D reconstruction, motion analysis, and camera calibration\u003c\/p\u003e\u003cp\u003e•\tProvides an introduction to four significant types of neural network, namely RBF, SOM, Hopfield, and deep neural networks\u003c\/p\u003e\u003cp\u003eThis all-encompassing survey offers a complete reference for all students, researchers, and practitioners involved in developing intelligent machine vision systems. The work is also an invaluable resource for professionals within the IT\/software and electronics industries involved in machine vision, imaging, and artificial intelligence.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eDr. Cosimo Distante is a Research Scientist in Computer Vision and Pattern Recognition in the Institute of Applied Sciences and Intelligent Systems (ISAI) at the Italian National Research Council (CNR). Dr. Arcangelo Distante is a researcher and the former Director of the Institute of Intelligent Systems for Automation (ISSIA) at the CNR. His research interests are in the fields of Computer Vision, Pattern Recognition, Machine Learning, and Neural Computation.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003eImage Formation Process\u003c\/p\u003e  \u003cp\u003eRadiometric Model\u003c\/p\u003e  \u003cp\u003eColor\u003c\/p\u003e  \u003cp\u003eOptical System\u003c\/p\u003e  \u003cp\u003eDigitization and Image Display\u003c\/p\u003e  \u003cp\u003eProperties of the Digital Image\u003c\/p\u003e  \u003cp\u003eData Organization\u003c\/p\u003e  \u003cp\u003eRepresentation and Description of Forms\u003c\/p\u003e  \u003cp\u003eImage Enhancement Techniques\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49396273119575,"sku":"9783030381479","price":999.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030381479.jpg?v=1791480478"},{"product_id":"individualbased-modeling-and-ecology-9780691096667","title":"Individualbased Modeling and Ecology","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eIndividual-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.\"\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\"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 Fisheries\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePreface 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","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403723678039,"sku":"9780691096667","price":73.8,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691096667.jpg?v=1730484360"},{"product_id":"computational-economics-9780691125497","title":"Computational Economics","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eDesigned 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.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\"Important and useful... [T]his book represents an excellent way to learn computational economics, doing it.\"--Pietro Terna, Journal of Artificial Societies and Social Simulation\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePreface 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","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403741372759,"sku":"9780691125497","price":103.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691125497.jpg?v=1730484413"},{"product_id":"dynamic-models-in-biology-9780691125893","title":"Dynamic Models in Biology","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003eFrom 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 \u003ci\u003eDynamic Models in Biology\u003c\/i\u003e, 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.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e  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.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e  Linked to a Web site with computer-lab materials and exercises, \u003ci\u003eDynamic Models in Biology\u003c\/i\u003e is a major new introduction to dynamic models for students in the biological sciences, mathematics, and engineering.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\"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 Conservation\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003eList 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","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403741700439,"sku":"9780691125893","price":73.6,"currency_code":"GBP","in_stock":true}]},{"product_id":"modeling-with-data-9780691133140","title":"Modeling with Data","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eExplains 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.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\"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, StudiaUBB\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePreface 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","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403752513879,"sku":"9780691133140","price":78.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691133140.jpg?v=1730484440"},{"product_id":"probability-markov-chains-queues-and-simulation-9780691140629","title":"Probability Markov Chains Queues and Simulation","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eOffers 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.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\"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 Statistics\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePreface 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","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403766833495,"sku":"9780691140629","price":106.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691140629.jpg?v=1730484480"},{"product_id":"introduction-to-modeling-convection-in-planets-and-stars-9780691141725","title":"Introduction to Modeling Convection in Planets","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eProvides 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.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\"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 MATH\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePreface 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","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403768471895,"sku":"9780691141725","price":106.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691141725.jpg?v=1730484486"},{"product_id":"numerical-methods-for-stochastic-computations-9780691142128","title":"Numerical Methods for Stochastic Computations","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eFocusing 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.\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\"[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 Reviews\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003ePreface 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","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403768832343,"sku":"9780691142128","price":54.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691142128.jpg?v=1730484486"},{"product_id":"text-as-data-9780691207544","title":"Text as Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Among the metaverse of possible books on \u003ci\u003eText as Data\u003c\/i\u003e 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.\"\u003cb\u003e---James Evans, \u003ci\u003eSociological Methods \u0026amp; Research\u003c\/i\u003e\u003c\/b\u003e","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":49403897381207,"sku":"9780691207544","price":71.4,"currency_code":"GBP","in_stock":true}]},{"product_id":"learning-google-analytics-9781098113087","title":"Learning Google Analytics","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eAuthor Mark Edmondson, Google Developer Expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49406791811415,"sku":"9781098113087","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098113087.jpg?v=1730497123"},{"product_id":"scaling-python-with-dask-9781098119874","title":"Scaling Python with Dask","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eWith 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.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49406792401239,"sku":"9781098119874","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098119874.jpg?v=1730497126"},{"product_id":"resilient-oracle-plsql-9781098134112","title":"Resilient Oracle PlSQL","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eThis 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.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49406793285975,"sku":"9781098134112","price":47.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098134112.jpg?v=1730497129"},{"product_id":"automating-data-quality-monitoring-at-scale-9781098145934","title":"Automating Data Quality Monitoring at Scale","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003eIn 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.","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":49406793941335,"sku":"9781098145934","price":39.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781098145934.jpg?v=1730497131"},{"product_id":"biological-knowledge-discovery-handbook-9781118132739","title":"Biological Knowledge Discovery Handbook","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003e\u003cb\u003eThe first comprehensive overview of preprocessing, mining, and postprocessing of biological data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMolecular 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.\u003c\/p\u003e \u003cp\u003eWritten by top experts, \u003ci\u003eBiological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eBIOLOGICAL DATA PREPROCESSING\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePart A: Biological Data\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTrade Review\u003c\/h2\u003e\n\u003cp\u003e“This book is a unique resource for practitioners and researchers in computer science, life science, and  mathematics.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 1 June 2015)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\n\u003cp\u003ePREFACE xiii\u003c\/p\u003e \u003cp\u003eCONTRIBUTORS xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSECTION I BIOLOGICAL DATA PREPROCESSING\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cb\u003ePART A: BIOLOGICAL DATA MANAGEMENT\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 GENOME AND TRANSCRIPTOME SEQUENCE DATABASES FOR DISCOVERY, STORAGE, AND REPRESENTATION OF ALTERNATIVE SPLICING EVENTS 5\u003cbr\u003e \u003ci\u003eBahar Taneri and Terry Gaasterland\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 CLEANING, INTEGRATING, AND WAREHOUSING GENOMIC DATA FROM BIOMEDICAL RESOURCES 35\u003cbr\u003e \u003ci\u003eFouzia Moussouni and Laure Berti-Equille\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 CLEANSING OF MASS SPECTROMETRY DATA FOR PROTEIN IDENTIFICATION AND QUANTIFICATION 59\u003cbr\u003e \u003ci\u003ePenghao Wang and Albert Y. Zomaya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 FILTERING PROTEIN–PROTEIN INTERACTIONS BY INTEGRATION OF ONTOLOGY DATA 77\u003cbr\u003e \u003ci\u003eYoung-Rae Cho\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART B: BIOLOGICAL DATA MODELING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5 COMPLEXITY AND SYMMETRIES IN DNA SEQUENCES 95\u003cbr\u003e \u003ci\u003eCarlo Cattani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 ONTOLOGY-DRIVEN FORMAL CONCEPTUAL DATA MODELING FOR BIOLOGICAL DATA ANALYSIS 129\u003cbr\u003e \u003ci\u003eCatharina Maria Keet\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 BIOLOGICAL DATA INTEGRATION USING NETWORK MODELS 155\u003cbr\u003e \u003ci\u003eGaurav Kumar and Shoba Ranganathan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 NETWORK MODELING OF STATISTICAL EPISTASIS 175\u003cbr\u003e \u003ci\u003eTing Hu and Jason H. Moore\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 GRAPHICAL MODELS FOR PROTEIN FUNCTION AND STRUCTURE PREDICTION 191\u003cbr\u003e \u003ci\u003eMingjie Tang, Kean Ming Tan, Xin Lu Tan, Lee Sael, Meghana Chitale, Juan Esquivel-Rodrýguez, and Daisuke Kihara\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART C: BIOLOGICAL FEATURE EXTRACTION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10 ALGORITHMS AND DATA STRUCTURES FOR NEXT-GENERATION SEQUENCES 225\u003cbr\u003e \u003ci\u003eFrancesco Vezzi, Giuseppe Lancia, and Alberto Policriti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 ALGORITHMS FOR NEXT-GENERATION SEQUENCING DATA 251\u003cbr\u003e \u003ci\u003eCostas S. Iliopoulos and Solon P. Pissis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 GENE REGULATORY NETWORK IDENTIFICATION WITH QUALITATIVE PROBABILISTIC NETWORKS 281\u003cbr\u003e \u003ci\u003eZina M. Ibrahim, Alioune Ngom, and Ahmed Y. Tawfik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART D: BIOLOGICAL FEATURE SELECTION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13 COMPARING, RANKING, AND FILTERING MOTIFS WITH\u003cbr\u003e CHARACTER CLASSES: APPLICATION TO BIOLOGICAL SEQUENCES ANALYSIS 309\u003cbr\u003e \u003ci\u003eMatteo Comin and Davide Verzotto\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 STABILITY OF FEATURE SELECTION ALGORITHMS AND ENSEMBLE FEATURE SELECTION METHODS IN\u003cbr\u003e BIOINFORMATICS 333\u003cbr\u003e \u003ci\u003ePengyi Yang, Bing B. Zhou, Jean Yee-Hwa Yang, and Albert Y. Zomaya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 STATISTICAL SIGNIFICANCE ASSESSMENT FOR BIOLOGICAL FEATURE SELECTION: METHODS AND ISSUES 353\u003cbr\u003e \u003ci\u003eJuntao Li, Kwok Pui Choi, Yudi Pawitan, and Radha Krishna Murthy Karuturi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16 SURVEY OF NOVEL FEATURE SELECTION METHODS FOR CANCER CLASSIFICATION 379\u003cbr\u003e \u003ci\u003eOleg Okun\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17 INFORMATION-THEORETIC GENE SELECTION IN EXPRESSION DATA 399\u003cbr\u003e \u003ci\u003ePatrick E. Meyer and Gianluca Bontempi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18 FEATURE SELECTION AND CLASSIFICATION FOR GENE EXPRESSION DATA USING EVOLUTIONARY COMPUTATION 421\u003cbr\u003e \u003ci\u003eHaider Banka, Suresh Dara, and Mourad Elloumi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSECTION II BIOLOGICAL DATA MINING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART E: REGRESSION ANALYSIS OF BIOLOGICAL DATA\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19 BUILDING VALID REGRESSION MODELS FOR BIOLOGICAL DATA USING STATA AND R 445\u003cbr\u003e \u003ci\u003eCharles Lindsey and Simon J. Sheather\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20 LOGISTIC REGRESSION IN GENOMEWIDE ASSOCIATION ANALYSIS 477\u003cbr\u003e \u003ci\u003eWentian Li and Yaning Yang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21 SEMIPARAMETRIC REGRESSION METHODS IN LONGITUDINAL DATA: APPLICATIONS TO AIDS CLINICAL TRIAL DATA 501\u003cbr\u003e \u003ci\u003eYehua Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART F: BIOLOGICAL DATA CLUSTERING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22 THE THREE STEPS OF CLUSTERING IN THE POST-GENOMIC ERA 521\u003cbr\u003e \u003ci\u003eRaffaele Giancarlo, Giosu´e Lo Bosco, Luca Pinello, and Filippo Utro\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23 CLUSTERING ALGORITHMS OF MICROARRAY DATA 557\u003cbr\u003e \u003ci\u003eHaifa Ben Saber, Mourad Elloumi, and Mohamed Nadif\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24 SPREAD OF EVALUATION MEASURES FOR MICROARRAY CLUSTERING 569\u003cbr\u003e \u003ci\u003eGiulia Bruno and Alessandro Fiori\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25 SURVEY ON BICLUSTERING OF GENE EXPRESSION DATA 591\u003cbr\u003e \u003ci\u003eAdelaide Valente Freitas, Wassim Ayadi, Mourad Elloumi, Jose Luis Oliveira, and Jin-Kao Hao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26 MULTIOBJECTIVE BICLUSTERING OF GENE EXPRESSION DATA WITH BIOINSPIRED ALGORITHMS 609\u003cbr\u003e \u003ci\u003eKhedidja Seridi, Laetitia Jourdan, and El-Ghazali Talbi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27 COCLUSTERING UNDER GENE ONTOLOGY DERIVED CONSTRAINTS FOR PATHWAY IDENTIFICATION 625\u003cbr\u003e \u003ci\u003eAlessia Visconti, Francesca Cordero, Dino Ienco, and Ruggero G. Pensa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART G: BIOLOGICAL DATA CLASSIFICATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28 SURVEY ON FINGERPRINT CLASSIFICATION METHODS FOR BIOLOGICAL SEQUENCES 645\u003cbr\u003e \u003ci\u003eBhaskar DasGupta and Lakshmi Kaligounder\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29 MICROARRAY DATA ANALYSIS: FROM PREPARATION TO CLASSIFICATION 657\u003cbr\u003e \u003ci\u003eLuciano Cascione, Alfredo Ferro, Rosalba Giugno, Giuseppe Pigola, and Alfredo Pulvirenti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e30 DIVERSIFIED CLASSIFIER FUSION TECHNIQUE FOR GENE EXPRESSION DATA 675\u003cbr\u003e \u003ci\u003eSashikala Mishra, Kailash Shaw, and Debahuti Mishra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e31 RNA CLASSIFICATION AND STRUCTURE PREDICTION: ALGORITHMS AND CASE STUDIES 685\u003cbr\u003e \u003ci\u003eLing Zhong, Junilda Spirollari, Jason T. L. Wang, and Dongrong Wen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e32 AB INITIO PROTEIN STRUCTURE PREDICTION: METHODS AND CHALLENGES 703\u003cbr\u003e \u003ci\u003eJad Abbass, Jean-Christophe Nebel, and Nashat Mansour\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e33 OVERVIEW OF CLASSIFICATION METHODS TO\u003cbr\u003e SUPPORT HIV\/AIDS CLINICAL DECISION MAKING 725\u003cbr\u003e \u003ci\u003eKhairul A. Kasmiran, Ali Al Mazari, Albert Y. Zomaya, and Roger J. Garsia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART H: ASSOCIATION RULES LEARNING FROM BIOLOGICAL DATA\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e34 MINING FREQUENT PATTERNS AND ASSOCIATION RULES FROM BIOLOGICAL DATA 737\u003cbr\u003e \u003ci\u003eIoannis Kavakiotis, George Tzanis, and Ioannis Vlahavas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e35 GALOIS CLOSURE BASED ASSOCIATION RULE MINING FROM BIOLOGICAL DATA 761\u003cbr\u003e \u003ci\u003eKartick Chandra Mondal and Nicolas Pasquier\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e36 INFERENCE OF GENE REGULATORY NETWORKS BASED ON ASSOCIATION RULES 803\u003cbr\u003e \u003ci\u003eCristian Andres Gallo, Jessica Andrea Carballido, and Ignacio Ponzoni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I: TEXT MINING AND APPLICATION TO BIOLOGICAL DATA\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e37 CURRENT METHODOLOGIES FOR BIOMEDICAL NAMED ENTITY RECOGNITION 841\u003cbr\u003e \u003ci\u003eDavid Campos, Sergio Matos, and José Luýs Oliveira\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e38 AUTOMATED ANNOTATION OF SCIENTIFIC DOCUMENTS: INCREASING ACCESS TO BIOLOGICAL KNOWLEDGE 869\u003cbr\u003e \u003ci\u003eEvangelos Pafilis, Heiko Horn, and Nigel P. Brown\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e39 AUGMENTING BIOLOGICAL TEXT MINING WITH SYMBOLIC INFERENCE 901\u003cbr\u003e \u003ci\u003eJong C. Park and Hee-Jin Lee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e40 WEB CONTENT MINING FOR LEARNING GENERIC RELATIONS AND THEIR ASSOCIATIONS FROM TEXTUAL BIOLOGICAL DATA 919\u003cbr\u003e \u003ci\u003eMuhammad Abulaish and Jahiruddin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e41 PROTEIN–PROTEIN RELATION EXTRACTION FROM BIOMEDICAL ABSTRACTS 943\u003cbr\u003e \u003ci\u003eSyed Toufeeq Ahmed, Hasan Davulcu, Sukru Tikves, Radhika Nair, and Chintan Patel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART J: HIGH-PERFORMANCE COMPUTING FOR BIOLOGICAL DATA MINING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e42 ACCELERATING PAIRWISE ALIGNMENT ALGORITHMS BY USING GRAPHICS PROCESSOR UNITS 971\u003cbr\u003e \u003ci\u003eMourad Elloumi, Mohamed Al Sayed Issa, and Ahmed Mokaddem\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e43 HIGH-PERFORMANCE COMPUTING IN HIGH-THROUGHPUT SEQUENCING 981\u003cbr\u003e \u003ci\u003eKamer Kaya, Ayat Hatem, Hatice Gulcin Ozer, Kun Huang, and Umit V. Catalyurek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e44 LARGE-SCALE CLUSTERING OF SHORT READS FOR METAGENOMICS ON GPUs 1003\u003cbr\u003e \u003ci\u003eThuy Diem Nguyen, Bertil Schmidt, Zejun Zheng, and Chee Keong Kwoh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSECTION III BIOLOGICAL DATA POSTPROCESSING\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART K: BIOLOGICAL KNOWLEDGE INTEGRATION AND VISUALIZATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e45 INTEGRATION OF METABOLIC KNOWLEDGE FOR GENOME-SCALE METABOLIC RECONSTRUCTION 1027\u003cbr\u003e \u003ci\u003eAli Masoudi-Nejad, Ali Salehzadeh-Yazdi, Shiva Akbari-Birgani, and Yazdan Asgari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e46 INFERRING AND POSTPROCESSING HUGE PHYLOGENIES 1049\u003cbr\u003e \u003ci\u003eStephen A. Smith and Alexandros Stamatakis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e47 BIOLOGICAL KNOWLEDGE VISUALIZATION 1073\u003cbr\u003e \u003ci\u003eRodrigo Santamarýa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e48 VISUALIZATION OF BIOLOGICAL KNOWLEDGE BASED ON MULTIMODAL BIOLOGICAL DATA 1109\u003cbr\u003e \u003ci\u003eHendrik Rohn and Falk Schreiber\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eINDEX 1127\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406834540887,"sku":"9781118132739","price":146.66,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118132739.jpg?v=1730497277"},{"product_id":"engineering-biostatistics-9781119168966","title":"Engineering Biostatistics","description":"\u003ch2\u003eBook Synopsis\u003c\/h2\u003e\u003cp\u003e\u003cb\u003eProvides a one-stop resource for engineers learning biostatistics using MATLAB and WinBUGS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThrough 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 reference\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e\u003ch2\u003eTable of Contents\u003c\/h2\u003e\u003cp\u003ePreface v\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter References 7\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Sample and Its Properties 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 9\u003c\/p\u003e \u003cp\u003e2.2 A MATLAB Session on Univariate Descriptive Statistics 10\u003c\/p\u003e \u003cp\u003e2.3 Location Measures 12\u003c\/p\u003e \u003cp\u003e2.4 Variability Measures 15\u003c\/p\u003e \u003cp\u003e2.4.1 Ranks 24\u003c\/p\u003e \u003cp\u003e2.5 Displaying Data 25\u003c\/p\u003e \u003cp\u003e2.6 Multidimensional Samples: Fisher’s Iris Data and Body Fat Data 29\u003c\/p\u003e \u003cp\u003e2.7 Multivariate Samples and Their Summaries 35\u003c\/p\u003e \u003cp\u003e2.8 Principal Components of Data 40\u003c\/p\u003e \u003cp\u003e2.9 Visualizing Multivariate Data 45\u003c\/p\u003e \u003cp\u003e2.10 Observations as Time Series 49\u003c\/p\u003e \u003cp\u003e2.11 About Data Types 52\u003c\/p\u003e \u003cp\u003e2.12 Big Data Paradigm 53\u003c\/p\u003e \u003cp\u003e2.13 Exercises 55\u003c\/p\u003e \u003cp\u003eChapter References 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Probability, Conditional Probability, and Bayes’ Rule 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 73\u003c\/p\u003e \u003cp\u003e3.2 Events and Probability 74\u003c\/p\u003e \u003cp\u003e3.3 Odds 85\u003c\/p\u003e \u003cp\u003e3.4 Venn Diagrams 86\u003c\/p\u003e \u003cp\u003e3.5 Counting Principles 88\u003c\/p\u003e \u003cp\u003e3.6 Conditional Probability and Independence 92\u003c\/p\u003e \u003cp\u003e3.6.1 Pairwise and Global Independence 97\u003c\/p\u003e \u003cp\u003e3.7 Total Probability 97\u003c\/p\u003e \u003cp\u003e3.8 Reassesing Probabilities: Bayes’ Rule 100\u003c\/p\u003e \u003cp\u003e3.9 Bayesian Networks 105\u003c\/p\u003e \u003cp\u003e3.10 Exercises 111\u003c\/p\u003e \u003cp\u003eChapter References 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Sensitivity, Specificity, and Relatives 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 133\u003c\/p\u003e \u003cp\u003e4.2 Notation 134\u003c\/p\u003e \u003cp\u003e4.2.1 Conditional Probability Notation 138\u003c\/p\u003e \u003cp\u003e4.3 Combining Two or More Tests 141\u003c\/p\u003e \u003cp\u003e4.4 ROC Curves 144\u003c\/p\u003e \u003cp\u003e4.5 Exercises 149\u003c\/p\u003e \u003cp\u003eChapter References 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Random Variables 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 159\u003c\/p\u003e \u003cp\u003e5.2 Discrete Random Variables 161\u003c\/p\u003e \u003cp\u003e5.2.1 Jointly Distributed Discrete Random Variables 166\u003c\/p\u003e \u003cp\u003e5.3 Some Standard Discrete Distributions 169\u003c\/p\u003e \u003cp\u003e5.3.1 Discrete Uniform Distribution 169\u003c\/p\u003e \u003cp\u003e5.3.2 Bernoulli and Binomial Distributions 170\u003c\/p\u003e \u003cp\u003e5.3.3 Hypergeometric Distribution 174\u003c\/p\u003e \u003cp\u003e5.3.4 Poisson Distribution 177\u003c\/p\u003e \u003cp\u003e5.3.5 Geometric Distribution 180\u003c\/p\u003e \u003cp\u003e5.3.6 Negative Binomial Distribution 183\u003c\/p\u003e \u003cp\u003e5.3.7 Multinomial Distribution 184\u003c\/p\u003e \u003cp\u003e5.3.8 Quantiles 186\u003c\/p\u003e \u003cp\u003e5.4 Continuous Random Variables 187\u003c\/p\u003e \u003cp\u003e5.4.1 Joint Distribution of Two Continuous Random Variables 192\u003c\/p\u003e \u003cp\u003e5.4.2 Conditional Expectation 193\u003c\/p\u003e \u003cp\u003e5.5 Some Standard Continuous Distributions 195\u003c\/p\u003e \u003cp\u003e5.5.1 Uniform Distribution 196\u003c\/p\u003e \u003cp\u003e5.5.2 Exponential Distribution 198\u003c\/p\u003e \u003cp\u003e5.5.3 Normal Distribution 200\u003c\/p\u003e \u003cp\u003e5.5.4 Gamma Distribution 201\u003c\/p\u003e \u003cp\u003e5.5.5 Inverse Gamma Distribution 203\u003c\/p\u003e \u003cp\u003e5.5.6 Beta Distribution 203\u003c\/p\u003e \u003cp\u003e5.5.7 Double Exponential Distribution 205\u003c\/p\u003e \u003cp\u003e5.5.8 Logistic Distribution 206\u003c\/p\u003e \u003cp\u003e5.5.9 Weibull Distribution 207\u003c\/p\u003e \u003cp\u003e5.5.10 Pareto Distribution 208\u003c\/p\u003e \u003cp\u003e5.5.11 Dirichlet Distribution 209\u003c\/p\u003e \u003cp\u003e5.6 Random Numbers and Probability Tables 210\u003c\/p\u003e \u003cp\u003e5.7 Transformations of Random Variables 211\u003c\/p\u003e \u003cp\u003e5.8 Mixtures 214\u003c\/p\u003e \u003cp\u003e5.9 Markov Chains 215\u003c\/p\u003e \u003cp\u003e5.10 Exercises 219\u003c\/p\u003e \u003cp\u003eChapter References 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Normal Distribution 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 235\u003c\/p\u003e \u003cp\u003e6.2 Normal Distribution 236\u003c\/p\u003e \u003cp\u003e6.2.1 Sigma Rules 240\u003c\/p\u003e \u003cp\u003e6.2.2 Bivariate Normal Distribution 241\u003c\/p\u003e \u003cp\u003e6.3 Examples with a Normal Distribution 243\u003c\/p\u003e \u003cp\u003e6.4 Combining Normal Random Variables 246\u003c\/p\u003e \u003cp\u003e6.5 Central Limit Theorem 249\u003c\/p\u003e \u003cp\u003e6.6 Distributions Related to Normal 253\u003c\/p\u003e \u003cp\u003e6.6.1 Chi-square Distribution 254\u003c\/p\u003e \u003cp\u003e6.6.2 t-Distribution 258\u003c\/p\u003e \u003cp\u003e6.6.3 Cauchy Distribution 259\u003c\/p\u003e \u003cp\u003e6.6.4 F-Distribution 260\u003c\/p\u003e \u003cp\u003e6.6.5 Noncentral χ2, t, and F Distributions 262\u003c\/p\u003e \u003cp\u003e6.6.6 Lognormal Distribution 263\u003c\/p\u003e \u003cp\u003e6.7 Delta Method and Variance Stabilizing Transformations 265\u003c\/p\u003e \u003cp\u003e6.8 Exercises 268\u003c\/p\u003e \u003cp\u003eChapter References 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Point and Interval Estimators 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 277\u003c\/p\u003e \u003cp\u003e7.2 Moment Matching and Maximum Likelihood Estimators 278\u003c\/p\u003e \u003cp\u003e7.2.1 Unbiasedness and Consistency of Estimators 285\u003c\/p\u003e \u003cp\u003e7.3 Estimation of a Mean, Variance, and Proportion 288\u003c\/p\u003e \u003cp\u003e7.3.1 Point Estimation of Mean 288\u003c\/p\u003e \u003cp\u003e7.3.2 Point Estimation of Variance 290\u003c\/p\u003e \u003cp\u003e7.3.3 Point Estimation of Population Proportion 294\u003c\/p\u003e \u003cp\u003e7.4 Confidence Intervals 295\u003c\/p\u003e \u003cp\u003e7.4.1 Confidence Intervals for the Normal Mean 296\u003c\/p\u003e \u003cp\u003e7.4.2 Confidence Interval for the Normal Variance 299\u003c\/p\u003e \u003cp\u003e7.4.3 Confidence Intervals for the Population Proportion . . . 302\u003c\/p\u003e \u003cp\u003e7.4.4 Confidence Intervals for Proportions When X = 0 306\u003c\/p\u003e \u003cp\u003e7.4.5 Designing the Sample Size with Confidence Intervals 307\u003c\/p\u003e \u003cp\u003e7.5 Prediction and Tolerance Intervals 309\u003c\/p\u003e \u003cp\u003e7.6 Confidence Intervals for Quantiles 311\u003c\/p\u003e \u003cp\u003e7.7 Confidence Intervals for the Poisson Rate 312\u003c\/p\u003e \u003cp\u003e7.8 Exercises 315\u003c\/p\u003e \u003cp\u003eChapter References 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Bayesian Approach to Inference 331\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 331\u003c\/p\u003e \u003cp\u003e8.2 Ingredients for Bayesian Inference 334\u003c\/p\u003e \u003cp\u003e8.3 Conjugate Priors 338\u003c\/p\u003e \u003cp\u003e8.4 Point Estimation 340\u003c\/p\u003e \u003cp\u003e8.4.1 Normal-Inverse Gamma Conjugate Analysis 343\u003c\/p\u003e \u003cp\u003e8.5 Prior Elicitation 345\u003c\/p\u003e \u003cp\u003e8.6 Bayesian Computation and Use of WinBUGS 348\u003c\/p\u003e \u003cp\u003e8.6.1 Zero Tricks in WinBUGS 351\u003c\/p\u003e \u003cp\u003e8.7 Bayesian Interval Estimation: Credible Sets 353\u003c\/p\u003e \u003cp\u003e8.8 Learning by Bayes’ Theorem 357\u003c\/p\u003e \u003cp\u003e8.9 Bayesian Prediction 358\u003c\/p\u003e \u003cp\u003e8.10 Consensus Means 362\u003c\/p\u003e \u003cp\u003e8.11 Exercises 365\u003c\/p\u003e \u003cp\u003eChapter References 372\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Testing Statistical Hypotheses 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 375\u003c\/p\u003e \u003cp\u003e9.2 Classical Testing Problem 377\u003c\/p\u003e \u003cp\u003e9.2.1 Choice of Null Hypothesis 377\u003c\/p\u003e \u003cp\u003e9.2.2 Test Statistic, Rejection Regions, Decisions, and Errors in Testing 379\u003c\/p\u003e \u003cp\u003e9.2.3 Power of the Test 380\u003c\/p\u003e \u003cp\u003e9.2.4 Fisherian Approach: p-Values 381\u003c\/p\u003e \u003cp\u003e9.3 Bayesian Approach to Testing 382\u003c\/p\u003e \u003cp\u003e9.3.1 Criticism and Calibration of p-Values 386\u003c\/p\u003e \u003cp\u003e9.4 Testing the Normal Mean 388\u003c\/p\u003e \u003cp\u003e9.4.1 z-Test 389\u003c\/p\u003e \u003cp\u003e9.4.2 Power Analysis of a z-Test 389\u003c\/p\u003e \u003cp\u003e9.4.3 Testing a Normal Mean When the Variance Is Not Known: t-Test 391\u003c\/p\u003e \u003cp\u003e9.4.4 Power Analysis of t-Test 394\u003c\/p\u003e \u003cp\u003e9.5 Testing Multivariate Mean: T-Square Test∗ 397\u003c\/p\u003e \u003cp\u003e9.5.1 T-Square Test 397\u003c\/p\u003e \u003cp\u003e9.5.2 Test for Symmetry 401\u003c\/p\u003e \u003cp\u003e9.6 Testing the Normal Variances 402\u003c\/p\u003e \u003cp\u003e9.7 Testing the Proportion 404\u003c\/p\u003e \u003cp\u003e9.7.1 Exact Test for Population Proportions 406\u003c\/p\u003e \u003cp\u003e9.7.2 Bayesian Test for Population Proportions 409\u003c\/p\u003e \u003cp\u003e9.8 Multiplicity in Testing, Bonferroni Correction, and False Discovery Rate 412\u003c\/p\u003e \u003cp\u003e9.9 Exercises 415\u003c\/p\u003e \u003cp\u003eChapter References 425\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Two Samples 427\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 427\u003c\/p\u003e \u003cp\u003e10.2 Means and Variances in Two Independent Normal Populations 428\u003c\/p\u003e \u003cp\u003e10.2.1 Confidence Interval for the Difference of Means 433\u003c\/p\u003e \u003cp\u003e10.2.2 Power Analysis for Testing Two Means 434\u003c\/p\u003e \u003cp\u003e10.2.3 More Complex Two-Sample Designs 438\u003c\/p\u003e \u003cp\u003e10.2.4 A Bayesian Test for Two Normal Means 439\u003c\/p\u003e \u003cp\u003e10.3 Testing the Equality of Normal Means When Samples Are Paired 443\u003c\/p\u003e \u003cp\u003e10.3.1 Sample Size in Paired t-Test 448\u003c\/p\u003e \u003cp\u003e10.3.2 Difference-in-Differences (DiD) Tests 449\u003c\/p\u003e \u003cp\u003e10.4 Two Multivariate Normal Means 451\u003c\/p\u003e \u003cp\u003e10.4.1 Confidence Intervals for Arbitrary Linear Combinations of Mean Differences 453\u003c\/p\u003e \u003cp\u003e10.4.2 Profile Analysis With Two Independent Groups 454\u003c\/p\u003e \u003cp\u003e10.4.3 Paired Multivariate Samples 456\u003c\/p\u003e \u003cp\u003e10.5 Two Normal Variances 459\u003c\/p\u003e \u003cp\u003e10.6 Comparing Two Proportions 463\u003c\/p\u003e \u003cp\u003e10.6.1 The Sample Size 465\u003c\/p\u003e \u003cp\u003e10.7 Risk Differences, Risk Ratios, and Odds Ratios 466\u003c\/p\u003e \u003cp\u003e10.7.1 Risk Differences 466\u003c\/p\u003e \u003cp\u003e10.7.2 Risk Ratio 467\u003c\/p\u003e \u003cp\u003e10.7.3 Odds Ratios 469\u003c\/p\u003e \u003cp\u003e10.7.4 Two Proportions from a Single Sample 473\u003c\/p\u003e \u003cp\u003e10.8 Two Poisson Rates 476\u003c\/p\u003e \u003cp\u003e10.9 Equivalence Tests 479\u003c\/p\u003e \u003cp\u003e10.10 Exercises 483\u003c\/p\u003e \u003cp\u003eChapter References 500\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 ANOVA and Elements of Experimental Design 503\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 503\u003c\/p\u003e \u003cp\u003e11.2 One-Way ANOVA 504\u003c\/p\u003e \u003cp\u003e11.2.1 ANOVA Table and Rationale for F-Test 506\u003c\/p\u003e \u003cp\u003e11.2.2 Testing Assumption of Equal Population Variances . . . 509\u003c\/p\u003e \u003cp\u003e11.2.3 The Null Hypothesis Is Rejected. What Next? 511\u003c\/p\u003e \u003cp\u003e11.2.4 Bayesian Solution 516\u003c\/p\u003e \u003cp\u003e11.2.5 Fixed- and Random-Effect ANOVA 518\u003c\/p\u003e \u003cp\u003e11.3 Welch’s ANOVA 518\u003c\/p\u003e \u003cp\u003e11.4 Two-Way ANOVA and Factorial Designs 521\u003c\/p\u003e \u003cp\u003e11.4.1 Two-way ANOVA: One Observation Per Cell 527\u003c\/p\u003e \u003cp\u003e11.5 Blocking 529\u003c\/p\u003e \u003cp\u003e11.6 Repeated Measures Design 531\u003c\/p\u003e \u003cp\u003e11.6.1 Sphericity Tests 534\u003c\/p\u003e \u003cp\u003e11.7 Nested Designs 535\u003c\/p\u003e \u003cp\u003e11.8 Power Analysis in ANOVA 539\u003c\/p\u003e \u003cp\u003e11.9 Functional ANOVA 545\u003c\/p\u003e \u003cp\u003e11.10 Analysis of Means (ANOM) 548\u003c\/p\u003e \u003cp\u003e11.11 Gauge R\u0026amp;R ANOVA 550\u003c\/p\u003e \u003cp\u003e11.12 Testing Equality of Several Proportions 556\u003c\/p\u003e \u003cp\u003e11.13 Testing the Equality of Several Poisson Means 557\u003c\/p\u003e \u003cp\u003e11.14 Exercises 559\u003c\/p\u003e \u003cp\u003eChapter References 582\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Models for Tables 585\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 586\u003c\/p\u003e \u003cp\u003e12.2 Contingency Tables: Testing for Independence 586\u003c\/p\u003e \u003cp\u003e12.2.1 Measuring Association in Contingency Tables 591\u003c\/p\u003e \u003cp\u003e12.2.2 Power Analysis for Contingency Tables 593\u003c\/p\u003e \u003cp\u003e12.2.3 Cohen’s Kappa 594\u003c\/p\u003e \u003cp\u003e12.3 Three-Way Tables 596\u003c\/p\u003e \u003cp\u003e12.4 Fisher’s Exact Test 600\u003c\/p\u003e \u003cp\u003e12.5 Stratified Tables: Mantel–Haenszel Test 603\u003c\/p\u003e \u003cp\u003e12.5.1 Testing Conditional Independence or Homogeneity . . . 604\u003c\/p\u003e \u003cp\u003e12.5.2 Odds Ratio from Stratified Tables 607\u003c\/p\u003e \u003cp\u003e12.6 Paired Tables: McNemar’s Test 608\u003c\/p\u003e \u003cp\u003e12.7 Risk Differences, Risk Ratios, and Odds Ratios for Paired Tables 610\u003c\/p\u003e \u003cp\u003e12.7.1 Risk Differences 610\u003c\/p\u003e \u003cp\u003e12.7.2 Risk Ratios 611\u003c\/p\u003e \u003cp\u003e12.7.3 Odds Ratios 612\u003c\/p\u003e \u003cp\u003e12.7.4 Liddell’s Procedure 617\u003c\/p\u003e \u003cp\u003e12.7.5 Garth Test 619\u003c\/p\u003e \u003cp\u003e12.7.6 Stuart–Maxwell Test 620\u003c\/p\u003e \u003cp\u003e12.7.7 Cochran’s Q Test∗ 626\u003c\/p\u003e \u003cp\u003e12.8 Exercises 628\u003c\/p\u003e \u003cp\u003eChapter References 643\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Correlation 647\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 647\u003c\/p\u003e \u003cp\u003e13.2 The Pearson Coefficient of Correlation 648\u003c\/p\u003e \u003cp\u003e13.2.1 Inference About ρ 650\u003c\/p\u003e \u003cp\u003e13.2.2 Bayesian Inference for Correlation Coefficients 663\u003c\/p\u003e \u003cp\u003e13.3 Spearman’s Coefficient of Correlation 665\u003c\/p\u003e \u003cp\u003e13.4 Kendall’s Tau 667\u003c\/p\u003e \u003cp\u003e13.5 Cum hoc ergo propter hoc 670\u003c\/p\u003e \u003cp\u003e13.6 Exercises 671\u003c\/p\u003e \u003cp\u003eChapter References 677\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Regression 679\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 679\u003c\/p\u003e \u003cp\u003e14.2 Simple Linear Regression 680\u003c\/p\u003e \u003cp\u003e14.2.1 Inference in Simple Linear Regression 688\u003c\/p\u003e \u003cp\u003e14.3 Calibration 697\u003c\/p\u003e \u003cp\u003e14.4 Testing the Equality of Two Slopes 699\u003c\/p\u003e \u003cp\u003e14.5 Multiple Regression 702\u003c\/p\u003e \u003cp\u003e14.5.1 Matrix Notation 703\u003c\/p\u003e \u003cp\u003e14.5.2 Residual Analysis, Influential Observations, Multicollinearity, and Variable Selection 709\u003c\/p\u003e \u003cp\u003e14.6 Sample Size in Regression 720\u003c\/p\u003e \u003cp\u003e14.7 Linear Regression That Is Nonlinear in Predictors 720\u003c\/p\u003e \u003cp\u003e14.8 Errors-In-Variables Linear Regression 723\u003c\/p\u003e \u003cp\u003e14.9 Analysis of Covariance 724\u003c\/p\u003e \u003cp\u003e14.9.1 Sample Size in ANCOVA 728\u003c\/p\u003e \u003cp\u003e14.9.2 Bayesian Approach to ANCOVA 729\u003c\/p\u003e \u003cp\u003e14.10 Exercises 731\u003c\/p\u003e \u003cp\u003eChapter References 748\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Regression for Binary and Count Data 751\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 751\u003c\/p\u003e \u003cp\u003e15.2 Logistic Regression 752\u003c\/p\u003e \u003cp\u003e15.2.1 Fitting Logistic Regression 753\u003c\/p\u003e \u003cp\u003e15.2.2 Assessing the Logistic Regression Fit 758\u003c\/p\u003e \u003cp\u003e15.2.3 Probit and Complementary Log-Log Links 769\u003c\/p\u003e \u003cp\u003e15.3 Poisson Regression 773\u003c\/p\u003e \u003cp\u003e15.4 Log-linear Models 779\u003c\/p\u003e \u003cp\u003e15.5 Exercises 783\u003c\/p\u003e \u003cp\u003eChapter References 798\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Inference for Censored Data and Survival Analysis 801\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 801\u003c\/p\u003e \u003cp\u003e16.2 Definitions 802\u003c\/p\u003e \u003cp\u003e16.3 Inference with Censored Observations 807\u003c\/p\u003e \u003cp\u003e16.3.1 Parametric Approach 807\u003c\/p\u003e \u003cp\u003e16.3.2 Nonparametric Approach: Kaplan–Meier or Product–Limit Estimator 809\u003c\/p\u003e \u003cp\u003e16.3.3 Comparing Survival Curves 815\u003c\/p\u003e \u003cp\u003e16.4 The Cox Proportional Hazards Model 818\u003c\/p\u003e \u003cp\u003e16.5 Bayesian Approach 822\u003c\/p\u003e \u003cp\u003e16.5.1 Survival Analysis in WinBUGS 823\u003c\/p\u003e \u003cp\u003e16.6 Exercises 829\u003c\/p\u003e \u003cp\u003eChapter References 835\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Goodness of Fit Tests 837\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 837\u003c\/p\u003e \u003cp\u003e17.2 Probability Plots 838\u003c\/p\u003e \u003cp\u003e17.2.1 Q–Q Plots 838\u003c\/p\u003e \u003cp\u003e17.2.2 P–P Plots 841\u003c\/p\u003e \u003cp\u003e17.2.3 Poissonness Plots 842\u003c\/p\u003e \u003cp\u003e17.3 Pearson’s Chi-Square Test 843\u003c\/p\u003e \u003cp\u003e17.4 Kolmogorov–Smirnov Tests 852\u003c\/p\u003e \u003cp\u003e17.4.1 Kolmogorov’s Test 852\u003c\/p\u003e \u003cp\u003e17.4.2 Smirnov’s Test to Compare Two Distributions 854\u003c\/p\u003e \u003cp\u003e17.5 Cramér-von Mises and Watson’s Tests 858\u003c\/p\u003e \u003cp\u003e17.5.1 Rosenblatt’s Test 860\u003c\/p\u003e \u003cp\u003e17.6 Moran’s Test 862\u003c\/p\u003e \u003cp\u003e17.7 Departures from Normality 863\u003c\/p\u003e \u003cp\u003e17.7.1 Ellimination of Unknown Parameters by Transformations 866\u003c\/p\u003e \u003cp\u003e17.8 Exercises 867\u003c\/p\u003e \u003cp\u003eChapter References 876\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Distribution-Free Methods 879\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 879\u003c\/p\u003e \u003cp\u003e18.2 Sign Test 880\u003c\/p\u003e \u003cp\u003e18.3 Wilcoxon Signed-Rank Test 884\u003c\/p\u003e \u003cp\u003e18.4 Wilcoxon Sum Rank Test and Mann–Whitney Test 887\u003c\/p\u003e \u003cp\u003e18.5 Kruskal–Wallis Test 890\u003c\/p\u003e \u003cp\u003e18.6 Friedman’s Test 894\u003c\/p\u003e \u003cp\u003e18.7 Resampling Methods 898\u003c\/p\u003e \u003cp\u003e18.7.1 The Jackknife 898\u003c\/p\u003e \u003cp\u003e18.7.2 Bootstrap 901\u003c\/p\u003e \u003cp\u003e18.7.3 Bootstrap Versions of Some Popular Tests 908\u003c\/p\u003e \u003cp\u003e18.7.4 Randomization and Permutation Tests 916\u003c\/p\u003e \u003cp\u003e18.7.5 Discussion 919\u003c\/p\u003e \u003cp\u003e18.8 Exercises 919\u003c\/p\u003e \u003cp\u003eChapter References 929\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Bayesian Inference Using Gibbs Sampling – BUGS Project 931\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 931\u003c\/p\u003e \u003cp\u003e19.2 Step-by-Step Session 932\u003c\/p\u003e \u003cp\u003e19.3 Built-in Functions and Common Distributions in WinBUGS 937\u003c\/p\u003e \u003cp\u003e19.4 MATBUGS: A MATLAB Interface to WinBUGS 938\u003c\/p\u003e \u003cp\u003e19.5 Exercises 942\u003c\/p\u003e \u003cp\u003eChapter References 943\u003c\/p\u003e \u003cp\u003eIndex 945\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407001919831,"sku":"9781119168966","price":90.86,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119168966.jpg?v=1791469793"}],"url":"https:\/\/bookcurl.com\/collections\/database-design-and-theory.oembed?page=4","provider":"Book Curl","version":"1.0","type":"link"}