Computer modelling and simulation Books

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Book SynopsisThis accessible, non-technical book reveals how, starting in the 1800s, scientists have used mathematical models and computer simulations to demonstrate that climate change is real and accelerating. Readers will learn where the key scientific ideas came from, how they were tested, and what future these models forecast for our planet.Trade Review'Numerical climate models are a critical tool for assessing the threat posed by climate change and investigating the options available to mitigate that threat. Yet, an understanding of these models-how they work, what they tell us, and how their tested and validated-has remained evasive for all but the most math and physics-literate. In Computing the Climate, computer scientist Steve Easterbook takes us on a journey through the world of climate modeling, making the science accessible to lay readers, and showing us why we should trust the models and heed their warnings, before it's too late.' Michael Mann, University of Pennsylvania, author of The New Climate War'Computing the Climate provides an impressively detailed history of how climate models evolved from simple equations calculated by hand to giant programs running on supercomputers. Avoiding jargon, this book explains to a general audience how the laws of physics and the principles of software engineering are combined to build climate models.' R. Saravanan, Texas A&M University, author of The Climate Demon'Computing the Climate takes a unique look at the history of computational modeling the Earth's climate system, the processes represented in these models, their evaluation, and how they are being used to project the potential changes in the future of our climate. When combined with more detailed analyses of concurrent issues being addressed in these models such as cloud and convection processes, this would be an excellent book for a university course on climate modeling.' Don Wuebbles, University of Illinois'I teach several courses in climate change and climate modeling for general and specialized audiences, and I am so excited to incorporate this new text by Easterbrook into those classes. While climate models are derived from first physical principles, climate models are developed by people and communities. I think that this book's approach of the tracing of revolutionary ideas and herculean efforts by generations of scientists to develop deep understanding and predictive capability for weather and climate does the topic justice. The logical progression of concepts, chapter by chapter is excellent as is the extensive, but not obtrusive, referencing throughout. Many difficult concepts, including: the greenhouse effect, chaos and predicability, computational instability, parallel computing, the difference between predictions and projections, are explained very well and accessibly. This book will be compelling reading both for students and people who simply want to know more.' Matthew Huber, Purdue University'Easterbrook's non-technical survey of climate modeling uniquely expands the climate change genre. Students will benefit from its broad scope and equation-free conceptual explanations, and climate modelers will appreciate its historical approach linking nineteenth century experiments and ideas to twenty-first century breakthroughs.' Baylor Fox-Kemper, Brown University'This is a very readable personal account of climate model development throughout history. It focuses on several individuals and modeling groups/countries. It often refers to 'you' and 'we'. I learned a lot and enjoyed the book, and I recommend it to anyone faced with making decisions involving the future climate.' Kevin Trenberth, University of Auckland, author of The Changing Flow of Energy Through the Climate System'This engaging, beautifully written book brings alive the scientists who created climate models, how they did it, and what the models can (and cannot) tell us - all in straightforward, nontechnical language and enlightening illustrations. If you want to understand how modern climate science works, start here.' Paul N. Edwards, Stanford University, author of A Vast Machine: Computer Models, Climate Data, and the Politics of Global WarmingTable of Contents1. Introduction; 2. The world's first climate model; 3. The forecast factory; 4. Taming chaos; 5. The heart of the machine; 6. The well-equipped physics lab; 7. Plug and play; 8. Sound science; 9. Choosing a future; References; Index.

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Book SynopsisA comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods. It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques An Introduction to Statistical Computing: Fully covers the traditional topics of statistical computing. Discusses both practical aspects and the theoretical background. Includes a chapter about conTrade Review"The exposition is quite clear, intuitive, and is a useful complement to more abstract treatises on stochastic calculus and simulation." (MathSciNet, 1 December 2015) “Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course.” (Zentralblatt MATH, 1 March 2014) “Statistical computing in its broadest sense is an ever-growing field far too extensive to be covered in a single text. The current book has a far more manageable scope, notwithstanding its title. Its focus is on the use of Monte Carlo methods to simulate random systems and explore statistical models.” (Mathematical Association of America, 1 January 2014) Table of ContentsList of algorithms ix Preface xi Nomenclature xiii 1 Random number generation 1 1.1 Pseudo random number generators 2 1.1.1 The linear congruential generator 2 1.1.2 Quality of pseudo random number generators 4 1.1.3 Pseudo random number generators in practice 8 1.2 Discrete distributions 8 1.3 The inverse transform method 11 1.4 Rejection sampling 15 1.4.1 Basic rejection sampling 15 1.4.2 Envelope rejection sampling 18 1.4.3 Conditional distributions 22 1.4.4 Geometric interpretation 26 1.5 Transformation of random variables 30 1.6 Special-purpose methods 36 1.7 Summary and further reading 36 Exercises 37 2 Simulating statistical models 41 2.1 Multivariate normal distributions 41 2.2 Hierarchical models 45 2.3 Markov chains 50 2.3.1 Discrete state space 51 2.3.2 Continuous state space 56 2.4 Poisson processes 58 2.5 Summary and further reading 67 Exercises 67 3 Monte Carlo methods 69 3.1 Studying models via simulation 69 3.2 Monte Carlo estimates 74 3.2.1 Computing Monte Carlo estimates 75 3.2.2 Monte Carlo error 76 3.2.3 Choice of sample size 80 3.2.4 Refined error bounds 82 3.3 Variance reduction methods 84 3.3.1 Importance sampling 84 3.3.2 Antithetic variables 88 3.3.3 Control variates 93 3.4 Applications to statistical inference 96 3.4.1 Point estimators 97 3.4.2 Confidence intervals 100 3.4.3 Hypothesis tests 103 3.5 Summary and further reading 106 Exercises 106 4 Markov Chain Monte Carlo methods 109 4.1 The Metropolis–Hastings method 110 4.1.1 Continuous state space 110 4.1.2 Discrete state space 113 4.1.3 Random walk Metropolis sampling 116 4.1.4 The independence sampler 119 4.1.5 Metropolis–Hastings with different move types 120 4.2 Convergence of Markov Chain Monte Carlo methods 125 4.2.1 Theoretical results 125 4.2.2 Practical considerations 129 4.3 Applications to Bayesian inference 137 4.4 The Gibbs sampler 141 4.4.1 Description of the method 141 4.4.2 Application to parameter estimation 146 4.4.3 Applications to image processing 151 4.5 Reversible Jump Markov Chain Monte Carlo 158 4.5.1 Description of the method 160 4.5.2 Bayesian inference for mixture distributions 171 4.6 Summary and further reading 178 4.6 Exercises 178 5 Beyond Monte Carlo 181 5.1 Approximate Bayesian Computation 181 5.1.1 Basic Approximate Bayesian Computation 182 5.1.2 Approximate Bayesian Computation with regression 188 5.2 Resampling methods 192 5.2.1 Bootstrap estimates 192 5.2.2 Applications to statistical inference 197 5.3 Summary and further reading 209 Exercises 209 6 Continuous-time models 213 6.1 Time discretisation 213 6.2 Brownian motion 214 6.2.1 Properties 216 6.2.2 Direct simulation 217 6.2.3 Interpolation and Brownian bridges 218 6.3 Geometric Brownian motion 221 6.4 Stochastic differential equations 224 6.4.1 Introduction 224 6.4.2 Stochastic analysis 226 6.4.3 Discretisation schemes 231 6.4.4 Discretisation error 236 6.5 Monte Carlo estimates 243 6.5.1 Basic Monte Carlo 243 6.5.2 Variance reduction methods 247 6.5.3 Multilevel Monte Carlo estimates 250 6.6 Application to option pricing 255 6.7 Summary and further reading 259 Exercises 260 Appendix A Probability reminders 263 A.1 Events and probability 263 A.2 Conditional probability 266 A.3 Expectation 268 A.4 Limit theorems 269 A.5 Further reading 270 Appendix B Programming in R 271 B.1 General advice 271 B.2 R as a Calculator 272 B.2.1 Mathematical operations 273 B.2.2 Variables 273 B.2.3 Data types 275 B.3 Programming principles 282 B.3.1 Don’t repeat yourself! 283 B.3.2 Divide and conquer! 286 B.3.3 Test your code! 290 B.4 Random number generation 292 B.5 Summary and further reading 294 Exercises 294 Appendix C Answers to the exercises 299 C.1 Answers for Chapter 1 299 C.2 Answers for Chapter 2 315 C.3 Answers for Chapter 3 319 C.4 Answers for Chapter 4 328 C.5 Answers for Chapter 5 342 C.6 Answers for Chapter 6 350 C.7 Answers for Appendix B 366 References 375 Index 379

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Book SynopsisA modern guide to computational models and constructive simulation for personalized patient care using the Digital Patient The healthcare industry's emphasis is shifting from merely reacting to disease to preventing disease and promoting wellness. Addressing one of the more hopeful Big Data undertakings, The Digital Patient: Advancing Healthcare, Research, and Education presents a timely resource on the construction and deployment of the Digital Patient and its effects on healthcare, research, and education. The Digital Patient will not be constructed based solely on new information from all the omics fields; it also includes systems analysis, Big Data, and the various efforts to model the human physiome and represent it virtually. The Digital Patient will be realized through the purposeful collaboration of patients as well as scientific, clinical, and policy researchers. The Digital Patient: Advancing Healthcare, Research, and Education addresses Table of ContentsList of Contributors xiii Preface xvii Part 1 The Vision: The Digital Patient—Improving Research, Development, Education, and Healthcare Practice 1 1 The Digital Patient 3C. Donald Combs Health, The Goal, 4 Personalized Medicine, 4 The Best Outcomes, 5 The Emergence of the Digital Patient, 5 The Human Physiome, 6 Enabling the Digital Patient, 8 P4 Medicine, 11 Conclusion, 11 References, 12 2 Reflecting on Discipulus and Remaining Challenges 15Vanessa Díaz]Zuccarini, Mona Alimohammadi, and César Pichardo]Almarza Introduction, 15 A Brief Contextual Background and a Call for Integration: Personalized Medicine is Holistic, 16 The Many Versions of the Digital Patient: On the Road to Medical Avatars, 18 Discipulus: The Digital Patient Technological Challenges and Main Conclusions, 19 The Remaining Challenges and Big Data, 24 Conclusion, 25 References, 26 3 Advancing the Digital Patient 27Catherine M. Banks Introduction, 27 The Digital Patient: Its Early Start, 28 Engaging the Digital Patient, 30 Conclusion, 31 4 The Significance of Modeling and Visualization 33John A. Sokolowski and Hector M. Garcia Introduction, 33 Modeling a Complex System: Human Physiology, 34 Medical Modeling, Simulation, and Visualization, 35 Modes and Types of Visualization, 40 Visualization for Patient]Specific Usefulness, 43 Conclusion, 43 References, 45 Part 2 State of the Art: Systems Biology, the Physiome and Personalized Health 49 5 The Visible Human: A Graphical Interface for Holistic Modeling and Simulation 51Victor M. Spitzer Introduction, 51 Education, 53 Modeling, 55 Virtual Reality Trainers and Simulators, 56 Conclusion, 58 References, 59 6 The Quantifiable Self: Petabyte by Petabyte 63C. Donald Combs and Scarlett R. Barham Introduction, 63 Smarr’s Quantified Self, 64 Extending Smarr’s Research, 67 The Quantified Self]Vision, Simplified, 69 Criticism, 69 Conclusion, 71 References, 72 7 Systems Biology and Health Systems Complexity: Implications for the Digital Patient 73C. Donald Combs, Scarlett R. Barham, and Peter M. A. Sloot Introduction, 73 Systems Biology, 75 The Institute for Systems Biology, 76 The Complexity Institute, 78 The Potential of Systems Biology, 81 Criticism, 82 Conclusion, 83 References, 83 8 Personalized Computational Modeling for the Treatment of Cardiac Arrhythmias 85Seth H. Weinberg Introduction, 85 Basics of Cardiac Electrophysiology, 86 Cardiac Modeling Advancements, 89 Regulation of Intracellular Calcium, 90 From Cells to Cables to Sheets to Tissue to the Heart, 91 Where Can we go from Here? What is the Cardiac Model in the Digital Patient? 95 References, 96 9 The Physiome Project, openEHR Archetypes, and the Digital Patient 101David P. Nickerson, Koray Atalag, Bernard de Bono, and Peter J. Hunter Introduction, 101 Multiscale Physiological Processes, 102 Physiome Project Standards, Repositories, and Tools, 103 Archetype Specialization, 112 Archetype Definition Language, 113 Linking Archetypes to External Knowledge Sources (Terminology and Biomedical Ontologies), 114 Archetype Annotations, 114 OpenEHR Model Repository and Governance, 115 Fast Healthcare Interoperability Resources, 115 A Disease Scenario, 116 Summary and Conclusions, 121 References, 122 10 Physics]Based Modeling for the Physiome 127William A. Pruett and Robert L. Hester Introduction, 127 Modeling Schemes, 128 Future Challenges, 142 Conclusion, 142 Acknowledgments, 143 References, 143 11 Modeling and Understanding the Human Body with SwarmScript 149Sebastian von Mammen, Stefan Schellmoser, Christian Jacob, and Jörg Hähner Introduction, 149 Related Work, 150 Multiagent Organization, 152 Designing Interactive Agents, 152 Speaking SwarmScript, 153 Answering Demand: The Design of SwarmScript, 153 Graph]Based Rule Representation, 153 The Source–Action–Target, 154 SwarmScript INTO3D, 154 A SwarmScript Dialogue, 155 Discussion, 159 Summary, 161 References, 162 12 Using Avatars and Agents to Promote Real]World Health Behavior Changes 167Sun Joo (Grace) Ahn Introduction, 167 Avatars and Agents, 168 Using Agents and Avatars to Promote Health Behavior Changes, 169 Conclusion, 174 References, 174 13 Virtual Reality and Eating, Diabetes, and Obesity 179Jessica E. Cornick and Jim Blascovich Introduction, 179 Virtual Reality, 179 Obesity and Weight Stigma, 184 Virtual Reality as a Tool for Combatting Health Issues, 185 Conclusion, 189 References, 189 14 Immersive Virtual Reality to Model Physical: Social Interaction and Self]Representation 197Eric B. Bauman Introduction, 197 Theory for Immersive Virtual Learning Spaces, 197 Conclusion, 202 References, 203 Part 3 Challenges: Assimilating the Comprehensive Digital Patient 205 15 A Roadmap for Building a Digital Patient System 207Saikou Y. Diallo and Christopher J. Lynch Introduction, 207 Approach, 210 Building the Digital Patient Through Interoperability, 211 Conclusion, 221 Acknowledgments, 221 References, 221 16 Multidisciplinary, Interdisciplinary, and Transdisciplinary Research: Contextualization and Reliability of the Composite 225Andreas Tolk Introduction, 225 Interdisciplinarity and Interdisciplinary Research, 226 Data Engineering to Support Interdisciplinarity and Interoperability, 228 Base Object Models to Support Transdisciplinarity and Composability, 233 Open Challenges on Reliability, 235 Summary and Conclusion, 237 References, 239 17 Bayes Net Modeling: The Means to Craft the Digital Patient 241Joseph A. Tatman and Barry C. Ezell Introduction, 241 Other Interesting Applications, 246 Conclusion, 251 References, 253 Part 4 Potential Impact: Engaging The Digital Patient 255 18 Virtual Reality Standardized Patients for Clinical Training 257Albert Rizzo and Thomas Talbot Introduction, 257 The Rationale for Virtual Standardized Patients, 258 Conversational Virtual Human Agents, 259 Usc Efforts to Create Virtual Standardized Patients, 260 Conclusion, 269 References, 270 19 The Digital Patient: Changing the Paradigm of Healthcare and Impacting Medical Research and Education 273V. Andrea Parodi Introduction, 273 Overview Digital Medicine Projects, 275 Personalized Patient Care Clinical Use, 279 Recommended Education and Training for VPH Project Participation, 281 From Flexner to the 2010 Carnegie Report, 284 Summary Statements, 286 References, 287 20 The Digital Patient: A Vision for Revolutionizing the Electronic Medical Record and Future Healthcare 289Richard M. Satava Introduction, 289 Applications of the Digital Patient as the EMR, 291 Discussion, 296 Conclusion, 297 References, 297 21 Realizing the Digital Patient 299C. Donald Combs and John A. Sokolowski Index 305

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Book SynopsisNeuroendocrinology with its well defined functions, inputs, and outputs, is one of the most fertile grounds for computational modeling in neuroscience. But modeling is often seen as something of a dark art.Table of ContentsList of Contributors, vii Series Preface, ix Preface, xi About the Companion Website, xv 1 Bridging Between Experiments and Equations: A Tutorial on Modeling Excitability, 1David P. McCobb and Mary Lou Zeeman 2 Ion Channels and Electrical Activity in Pituitary Cells: A Modeling Perspective, 80Richard Bertram, Joël Tabak, and Stanko S. Stojilkovic 3 Endoplasmic Reticulum- and Plasma-Membrane-Driven Calcium Oscillations, 111Arthur Sherman 4 A Mathematical Model of Gonadotropin-Releasing Hormone Neurons, 142James Sneyd, Wen Duan, and Allan Herbison 5 Modeling Spiking and Secretion in the Magnocellular Vasopressin Neuron, 166Duncan J. MacGregor and Gareth Leng 6 Modeling Endocrine Cell Network Topology, 206David J. Hodson, Francois Molino, and Patrice Mollard 7 Modeling the Milk-Ejection Reflex, 227Gareth Leng and Jianfeng Feng 8 Dynamics of the HPA Axis: A Systems Modeling Approach, 252John R. Terry, Jamie J. Walker, Francesca Spiga, and Stafford L. Lightman 9 Modeling the Dynamics of Gonadotropin-Releasing Hormone (GnRH) Secretion in the Course of an Ovarian Cycle, 284Frédérique Clément and Alexandre Vidal Glossary, 305 Index, 315

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