Mathematical modelling Books

Book SynopsisA bold, visionary, and mind-bending exploration of how the geometry of chaos can explain our uncertain worldfrom weather and pandemics to quantum physics and free willCovering a breathtaking range of topicsfrom climate change to the foundations of quantum physics, from economic modelling to conflict prediction, from free will to consciousness and spiritualityThe Primacy of Doubt takes us on a unique journey through the science of uncertainty. A key theme that unifies these seemingly unconnected topics is the geometry of chaos: the beautiful and profound fractal structures that lie at the heart of much of modern mathematics. Royal Society Research Professor Tim Palmer shows us how the geometry of chaos not only provides the means to predict the world around us, it suggests new insights into some of the most astonishing aspects of our universe and ourselves. This important and timely book helps the reader makes sense of uncertainty in a rapidly changing world.Trade ReviewThe Primacy of Doubt provides a remarkably broad-ranging account of uncertainty in physics, in all its various aspects. I strongly recommend this highly thought-provoking book. * Roger Penrose, OM, FRS, winner of the 2020 Nobel Prize in Physics *Tim Palmer is a scientific polymath. It's hard to think of anyone else who could have written so authoritatively—and so accessibly—on themes extending from quantum gravity to climate modelling. This fascinating and important book offers some profoundly original speculations on conceptual linkages across different sciences. * Lord Martin Rees, Astronomer Royal of the United Kingdom *In a whirlwind of a book that's partly scientific autobiography and partly the manifest of a visionary, Tim Palmer masterfillly weaves together climate change and quantum mechanics into one coherent whole. Using uncertainty as a unifying principle, Palmer puts forward new perspectives on old problems. A revolutionary thinker way ahead of his time. * Sabine Hossenfelder, author of Lost in Math *The Primacy of Doubt is an important book by one of the pioneers of dynamical weather prediction, indispensable for daily life. * Suki Manabe, winner of the 2021 Nobel Prize in Physics *Quite possibly the best popular science book I've ever read... The Primacy of Doubt is like getting off one of those exciting roller coaster rides, when your immediate inclination is to think 'I want to do that again, but I'll have a bit of a break first.' I will be reading this book again, without doubt. Remarkable. * Brian Clegg, Popular Science *important book * Andrew Robinson, Nature *Physicist Palmer delivers a challenging but rewarding look at how uncertainty helps scientists make sense of the world ... Despite the complexity of his arguments, the author succeeds at bringing complicated theories within reach of those who have a basic familiarity with physics. Science-minded readers, take note. * Publishers Weekly *The Primacy of Doubt also contains very informative explanations as to the application of chaos theory in climate and meteorological models, and why meteorologists failed to predict southern Britain's 1987 hurricane. To my mind this were probably the book's strongest areas and are 'must reads' for those with an interest in climate forecasting. * Jonathan Cowie, SF2 Concatenation *delightful and substantive * William Hooke, Living on the Real World *An exploration of the amorphous concept of uncertainty... [an] informative, ingenious book. * Kirkus Reviews *Provocative... useful for scientists and non-scientists alike * Jessica Flack, Physics World *Table of ContentsIntroduction Part I: The Science of Uncertainty 1: Chaos, Chaos Everywhere 2: The Geometry of Chaos 3: Noisy, Million-Dollar Butterflies 4: Quantum Uncertainty: Reality Lost? Part II: Predicting our Chaotic World 5: The Two Roads to Monte Carlo 6: Climate Change: Catastrophe or Just Lukewarm? 7: Pandemics 8: Financial Crashes 9: Deadly Conflict and the Digital Ensemble of Spaceship Earth 10: Decisions! Decisions! Part III: Understanding the Chaotic Universe and our Place in it 11: Quantum Uncertainty: Reality Regained? 12: Our Noisy Brains 13: Free Will, Consciousness, and God

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Book SynopsisThe human brain is made up of 85 billion neurons, which are connected by over 100 trillion synapses. For more than a century, a diverse array of researchers searched for a language that could be used to capture the essence of what these neurons do and how they communicate and how those communications create thoughts, perceptions and actions. The language they were looking for was mathematics, and we would not be able to understand the brain as we do today without it.In Models of the Mind, author and computational neuroscientist Grace Lindsay explains how mathematical models have allowed scientists to understand and describe many of the brain''s processes, including decision-making, sensory processing, quantifying memory, and more. She introduces readers to the most important concepts in modern neuroscience, and highlights the tensions that arise when the abstract world of mathematical modelling collides with the messy details of biology. Each chapter of MoTrade ReviewGrace Lindsay provides a masterful tour of this important frontier, tackling intimidating topics with verve and wit. * Sean Carroll *This is a remarkable book … an excellent introduction to an area that few of us probably know anything about, and all the more fascinating because of that. * Popular Science *Models of the Mind is a grand tour through the history of computational neuroscience, from its humble beginnings in information theory and neuron structure up to its modern manifestations harnessing supercomputers to run large scale convolutional neural networks that model important brain systems. * Women You Should Know *The book is not only wide-ranging in its choice of topics but is also a lively journey through the history of these efforts and traces the lives of the eccentric and fascinating scientists who were instrumental in figuring out the brain’s working by using tools ranging from information theory and graph theory to Bayesian modeling and neural networks. * 3 Quarks Daily * ‘Enthralling, erudite and accessible … an engrossing history of science and an enlightening guide to neuroscience’s current frontiers.’ * Liam Drew, Neurobiologist and author of I, Mammal: The Story of What Makes Us Mammals *‘This book is an anthology of the scientific poetry that has illuminated our studies and conceptions of the brain.’ * Professor Larry Abbott, Center for Theoretical Neuroscience, Columbia University *Table of Contents1: Spherical Cows 2: How Neurons Get Their Spike 3: Learning to Compute 4: Making and Maintaining Memories 5: Excitation and Inhibition 6: Stages of Sight 7: Cracking the Neural Code 8: Movement in Low Dimensions 9: From Structure to Function 10: Making Rational Decisions 11: How Rewards Guide Actions 12: Grand Unified Theories of the Brain Mathematical Appendix Acknowledgements Bibliography Index

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Book SynopsisThis hands-on introduction to numerical geodynamic modelling provides a solid grounding in the necessary mathematical theory and techniques, including continuum mechanics and partial differential equations, before introducing key numerical modelling methods and applications. Fully updated, this second edition includes four completely new chapters covering the most recent advances in modelling inertial processes, seismic cycles and fluid-solid interactions, and the development of adaptive mesh refinement algorithms. Many well-documented, state-of-the-art visco-elasto-plastic 2D models are presented, which allow robust modelling of key geodynamic processes. Requiring only minimal prerequisite mathematical training, and featuring over sixty practical exercises and ninety MATLAB examples, this user-friendly resource encourages experimentation with geodynamic models. It is an ideal introduction for advanced courses and can be used as a self-study aid for graduates seeking to master geodynamic modelling for their own research projects.Trade Review'A great introduction to computational geodynamics with vivid examples, hands-on exercises and step-by-step derivations of formulas. Even better than the first edition.' Sascha Brune, Das Helmholtz-Zentrum Potsdam – Deutsches GeoForschungsZentrum'This book is so much more than an introduction to geodynamic modelling. Taras Gerya opens the world of geodynamic experiments by taking the reader through a carefully designed set of hands-on programming exercises that will convince you that modelling is not terribly complicated, but a process to logically follow through. Go ahead and get started!' Susanne Buiter, Geological Survey of Norway'This comprehensive textbook challenges all solid Earth scientists to give geodynamic modelling a try in a hands-on, empowering style. The new edition covers even more ground, including cutting-edge topics. A great achievement, and the community will be the better for it.' Thorsten Becker, University of Texas, AustinPraise for the first edition: '… the book provides excellent value for those wanting an introduction to the field. Anyone who works carefully through this book and completes all the exercises should be well prepared for further work in geodynamic modelling.' GeoscientistPraise for the first edition: 'The book is written in a light and engaging style such that it deserves a place on the recommended reading list of any undergraduate or Masters course that includes geodynamics. Additionally, it will be a valuable resource for any geoscientist who wants to include geodynamic modelling within their research activities.' Geological MagazineTable of Contents1. The continuity equation; 2. Density and gravity; 3. Numerical solutions of partial differential equations; 4. Stress and strain; 5. The momentum equation; 6. Viscous rheology of rocks; 7. Numerical solutions of the momentum and continuity equations; 8. The advection equation and marker-in-cell method; 9. The heat conservation equation; 10. Numerical solution of the heat conservation equation; 11. 2D thermomechanical code structure; 12. Elasticity and plasticity; 13. 2D implementation of visco-elasto-plasticity; 14. 2D thermomechanical modelling of inertial processes; 15. Seismo-thermomechanical modelling; 16. Hydro-thermomechanical modelling; 17. Adaptive mesh refinement; 18. The multigrid method; 19. Programming of 3D problems; 20. Numerical benchmarks; 21. Design of 2D numerical geodynamic models; Epilogue: outlook; Appendix: MATLAB® program examples; References; Index.

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Book SynopsisThe study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on an unprecedented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and central developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.Topics covered include the measurement of networks; methods for analyzing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms; mathematical models of networks, including random graph models and generative models; and theories of dynamical processes taking place on networks.Trade ReviewThis is the definitive book on networks, friendly enough for anyone to read and serious enough for researchers to find their way. [Newman] is one of the founders and leaders of the field and has updated the book with cutting-edge topics. * Professor Cris Moore, Santa Fe Institute *This is the definitive book on network science, by one of its most brilliant researchers and graceful expositors. The second edition of Mark Newman's Networks is clear, comprehensive, and fascinating. * Steven Strogatz, Department of Mathematics, Cornell University, USA *This is an excellent textbook by one of the preeminent scholars in the study of networks. I draw heavily from it when teaching my undergraduate course on networks, and I am very pleased to see a new edition of the book. Newman's clear exposition shines through in this textbook. * Mason Porter, Department of Mathematics, UCLA, USA *An extraordinarily comprehensive and clear exposition of network science from one of the giants in the field. Newman succeeds in making accessible to a broad readership even the most technical content. * Santo Fortunato, School of Informatics and Computing, Indiana University *Reviews from previous edition:Networks accomplishes two key goals: It provides a comprehensive introduction and presents the theoretic backbone of network science. [] The book is balanced in its presentation of theoretical concepts, computational techniques, and algorithms. The level of difficulty increases which each chapter [which] makes the book particularly valuable to physics students who wish to acquire a solid foundation based on their knowledge of basic linear algebra, calculus, and differential equations. * Physics Today *Newman has written a wonderful book that gives an extensive overview of the broadly interdisciplinary network-related developments that have occured in many fields, including mathematics, physics, computer science, biology, and the social sciences ... Overall, a valuable resource covering a wide-randing field. * Choice *Likely to become the standard introductory textbook for the study of networks [...] Overall, this is an excellent textbook for the growing field of networks. It is cleverly written and suitable as both an introduction for undergraduate students (particularly Parts 1 to 3) and as a roadmap for graduate students. [...] Being highly self-contained, computer scientists and professionals from other fields can also use the book - in fact, the author himself is a physicist. In short, this book is a delight for the inquisitive mind. * Computing Reviews *This book brings together, for the first time, the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong connections between work in different subject areas. * CERN Courier *Table of Contents1: Introduction Part I: The empirical study of networks 2: Technological networks 3: Networks of information 4: Social networks 5: Biological networks Part II: Fundamentals of network theory 6: Mathematics of networks 7: Measures and metrics 8: Computer algorithms 9: Network statistics and measurement error 10: The structure of real-world networks Part III: Network models 11: Random graphs 12: The configuration model 13: Models of network formation Part IV: Applications 14: Community structure 15: Percolation and network resilience 16: Epidemics on networks 17: Dynamical systems on networks 18: Network search

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Book SynopsisBayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods--in language ecologists can understand. Unlike other books on the subject, this one emphasTrade Review"A refreshing and solid read for anyone confused or distracted by Bayesian recipe books."--Carsten F. Dormann, Quarterly Review of BiologyTable of ContentsPreface ix I Fundamentals 1 1 PREVIEW 3 1.1 A Line of Inference for Ecology 4 1.2 An Example Hierarchical Model 11 1.3 What Lies Ahead? 15 2 DETERMINISTIC MODELS 17 2.1 Modeling Styles in Ecology 17 2.2 A Few Good Functions 21 3 PRINCIPLES OF PROBABILITY 29 3.1 Why Bother with First Principles? 29 3.2 Rules of Probability 31 3.3 Factoring Joint Probabilities 36 3.4 Probability Distributions 39 4 LIKELIHOOD 71 4.1 Likelihood Functions 71 4.2 Likelihood Profiles 74 4.3 Maximum Likelihood 76 4.4 The Use of Prior Information in Maximum Likelihood 77 5 SIMPLE BAYESIAN MODELS 79 5.1 Bayes' Theorem 81 5.2 The Relationship between Likelihood and Bayes' 85 5.3 Finding the Posterior Distribution in Closed Form 86 5.4 More about Prior Distributions 90 6 HIERARCHICAL BAYESIAN MODELS 107 6.1 What Is a Hierarchical Model? 108 6.2 Example Hierarchical Models 109 6.3 When Are Observation and Process Variance Identifiable? 141 II Implementation 143 7 MARKOV CHAIN MONTE CARLO 145 7.1 Overview 145 7.2 How Does MCMC Work? 146 7.3 Specifics of the MCMC Algorithm 150 7.4 MCMC in Practice 177 8 INFERENCE FROM A SINGLE MODEL 181 8.1 Model Checking 181 8.2 Marginal Posterior Distributions 190 8.3 Derived Quantities 194 8.4 Predictions of Unobserved Quantities 196 8.5 Return to the Wildebeest 201 9 INFERENCE FROM MULTIPLE MODELS 209 9.1 Model Selection 210 9.2 Model Probabilities and Model Averaging 222 9.3 Which Method to Use? 227 III Practice in Model Building 231 10 WRITING BAYESIAN MODELS 233 10.1 A General Approach 233 10.2 An Example of Model Building: Aboveground Net Primary Production in Sagebrush Steppe 237 11 PROBLEMS 243 11.1 Fisher's Ticks 244 11.2 Light Limitation of Trees 245 11.3 Landscape Occupancy of Swiss Breeding Birds 246 11.4 Allometry of Savanna Trees 247 11.5 Movement of Seals in the North Atlantic 248 12 SOLUTIONS 251 12.1 Fisher's Ticks 251 12.2 Light Limitation of Trees 256 12.3 Landscape Occupancy of Swiss Breeding Birds 259 12.4 Allometry of Savanna Trees 264 12.5 Movement of Seals in the North Atlantic 268 Afterword 273 Acknowledgments 277 A Probability Distributions and Conjugate Priors 279 Bibliography 283 Index 293

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Book Synopsis"Modeling the Environment" was the first textbook in an emerging field - the modeling techniques that allow managers and researchers to see in advance the consequences of actions and policies in environmental management. This new edition brings the book thoroughly up to date and reaffirms its status as the leading introductory text on the subject. System dynamics is one of the most widely used methods of modeling. The fundamental principles of this approach are demonstrated here with a wide range of examples, including geohydrology, population biology, epidemiology, and economics. The applications demonstrate the transferability of the systems approach across disciplines, across spatial scales, and across time scales. All of the models are implemented with stock and flow software programs such as Stella and Vensim, which are easy for students to learn and use.

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Book SynopsisThis book provides a user-friendly, hands-on introduction to the Nonlinear Mixed Effects Modeling (NONMEM) system, the most powerful tool for pharmacokinetic / pharmacodynamic analysis. Introduces requisite background to using Nonlinear Mixed Effects Modeling (NONMEM), covering data requirements, model building and evaluation, and quality control aspects Provides examples of nonlinear modeling concepts and estimation basics with discussion on the model building process and applications of empirical Bayesian estimates in the drug development environment Includes detailed chapters on data set structure, developing control streams for modeling and simulation, model applications, interpretation of NONMEM output and results, and quality control Has datasets, programming code, and practice exercises with solutions, available on a supplementary websiteTrade Review“This book may make the “User Guide V experience” a story from the good old times for the next generation of pharmacometricians.” (CPT: Pharmacometrics & Systems Pharmacology, 22 December 2014)Table of ContentsPreface xiii CHAPTER 1 The Practice of Pharmacometrics 1 1.1 Introduction 1 1.2 Applications of Sparse Data Analysis 2 1.3 Impact of Pharmacometrics 4 1.4 Clinical Example 5 CHAPTER 2 Population Model Concepts and Terminology 9 2.1 Introduction 9 2.2 Model Elements 10 2.3 Individual Subject Models 11 2.4 Population Models 12 2.4.1 Fixed-Effect Parameters 13 2.4.2 Random-Effect Parameters 14 2.5 Models of Random Between-Subject Variability (L1) 17 2.5.1 Additive Variation 17 2.5.2 Constant Coefficient of Variation 18 2.5.3 Exponential Variation 18 2.5.4 Modeling Sources of Between-Subject Variation 19 2.6 Models of Random Variability in Observations (L2) 19 2.6.1 Additive Variation 20 2.6.2 Constant Coefficient of Variation 21 2.6.3 Additive Plus CCV Model 22 2.6.4 Log-Error Model 24 2.6.5 Relationship Between RV Expressions and Predicted Concentrations 24 2.6.6 Significance of the Magnitude of RV 25 2.7 Estimation Methods 26 2.8 Objective Function 26 2.9 Bayesian Estimation 27 CHAPTER 3 NONMEM Overview and Writing an NM-TRAN Control Stream 28 3.1 Introduction 28 3.2 Components of the NONMEM System 28 3.3 General Rules 30 3.4 Required Control Stream Components 31 3.4.1 $PROBLEM Record 31 3.4.2 The $DATA Record 32 3.4.3 The $INPUT Record 35 3.5 Specifying the Model in NM-TRAN 35 3.5.1 Calling PREDPP Subroutines for Specific PK Models 35 3.5.2 Specifying the Model in the $PK Block 38 3.5.3 Specifying Residual Variability in the $ERROR Block 45 3.5.4 Specifying Models Using the $PRED Block 49 3.6 Specifying Initial Estimates with $THETA, $OMEGA, and $SIGMA 50 3.7 Requesting Estimation and Related Options 56 3.8 Requesting Estimates of the Precision of Parameter Estimates 62 3.9 Controlling the Output 63 CHAPTER 4 Datasets 66 4.1 Introduction 66 4.2 Arrangement of the Dataset 68 4.3 Variables of the Dataset 71 4.3.1 TIME 71 4.3.2 DATE 71 4.3.3 ID 72 4.3.4 DV 74 4.3.5 MDV 74 4.3.6 CMT 74 4.3.7 EVID 75 4.3.8 AMT 76 4.3.9 RATE 77 4.3.10 ADDL 78 4.3.11 II 79 4.3.12 SS 80 4.4 Constructing Datasets with Flexibility to Apply Alternate Models 80 4.5 Examples of Event Records 81 4.5.1 Alternatives for Specifying Time 81 4.5.2 Infusions and Zero-Order Input 81 4.5.3 Using ADDL 82 4.5.4 Steady-State Approach 83 4.5.5 Samples Before and After Achieving Steady State 83 4.5.6 Unscheduled Doses in a Steady-State Regimen 84 4.5.7 Steady-State Dosing with an Irregular Dosing Interval 84 4.5.8 Multiple Routes of Administration 85 4.5.9 Modeling Multiple Dependent Variable Data Types 86 4.5.10 Dataset for $PRED 86 4.6 Beyond Doses and Observations 87 4.6.1 Other Data Items 87 4.6.2 Covariate Changes over Time 88 4.6.3 Inclusion of a Header Row 89 CHAPTER 5 Model Building: Typical Process 90 5.1 Introduction 90 5.2 Analysis Planning 90 5.3 Analysis Dataset Creation 92 5.4 Dataset Quality Control 93 5.5 Exploratory Data Analysis 94 5.5.1 EDA: Population Description 95 5.5.2 EDA: Dose-Related Data 99 5.5.3 EDA: Concentration-Related Data 99 5.5.4 EDA: Considerations with Large Datasets 111 5.5.5 EDA: Summary 115 5.6 Base Model Development 116 5.6.1 Standard Model Diagnostic Plots and Interpretation 116 5.6.2 Estimation of Random Effects 130 5.6.3 Precision of Parameter Estimates (Based on $COV Step) 137 5.7 Covariate Evaluation 138 5.7.1 Covariate Evaluation Methodologies 140 5.7.2 Statistical Basis for Covariate Selection 141 5.7.3 Diagnostic Plots to Illustrate Parameter-Covariate Relationships 143 5.7.4 Typical Functional Forms for Covariate-Parameter Relationships 148 5.7.5 Centering Covariate Effects 156 5.7.6 Forward Selection Process 160 5.7.7 Evaluation of the Full Multivariable Model 167 5.7.8 Backward Elimination Process 169 5.7.9 Other Covariate Evaluation Approaches 171 5.8 Model Refinement 172 CHAPTER 6 Interpreting the NONMEM Output 178 6.1 Introduction 178 6.2 Description of the Output Files 178 6.3 The NONMEM Report File 179 6.3.1 NONMEM-Related Output 179 6.3.2 PREDPP-Related Output 180 6.3.3 Output from Monitoring of the Search 180 6.3.4 Minimum Value of the Objective Function and Final Parameter Estimates 182 6.3.5 Covariance Step Output 186 6.3.6 Additional Output 187 6.4 Error Messages: Interpretation and Resolution 188 6.4.1 NM-TRAN Errors 188 6.4.2 $ESTIMATION Step Failures 189 6.4.3 $COVARIANCE Step Failures 190 6.4.4 PREDPP Errors 191 6.4.5 Other Types of NONMEM Errors 192 6.4.6 FORTRAN Compiler or Other Run-Time Errors 193 6.5 General Suggestions for Diagnosing Problems 193 CHAPTER 7 App lications Using Parameter Estimates from the Individual 198 7.1 Introduction 198 7.2 Bayes Theorem and Individual Parameter Estimates 200 7.3 Obtaining Individual Parameter Estimates 202 7.4 Applications of Individual Parameter Estimates 204 7.4.1 Generating Subject-Specific Exposure Estimates 204 7.4.2 Individual Exposure Estimates for Group Comparisons 210 CHAPTER 8 Introduction to Model Evaluation 212 8.1 Introduction 212 8.2 Internal Validation 212 8.3 External Validation 213 8.4 Predictive Performance Assessment 214 8.5 Objective Function Mapping 217 8.6 Leverage Analysis 220 8.7 Bootstrap Procedures 222 8.8 Visual and Numerical Predictive Check Procedures 223 8.8.1 The VPC Procedure 223 8.8.2 Presentation of VPC Results 225 8.8.3 The Numerical Predictive Check (NPC) Procedure 229 8.9 Posterior Predictive Check Procedures 229 CHAPTER 9 User-Written Models 232 9.1 Introduction 232 9.2 $MODEL 235 9.3 $SUBROUTINES 236 9.3.1 General Linear Models (ADVAN5 and ADVAN7) 236 9.3.2 General Nonlinear Models (ADVAN6, ADVAN8, ADVAN9, and ADVAN13) 238 9.3.3 $DES 238 9.4 A Series of Examples 240 9.4.1 Defined Fractions Absorbed by Zero- and First-Order Processes 240 9.4.2 Sequential Absorption with First-Order Rates, without Defined Fractions 242 9.4.3 Parallel Zero-Order and First-Order Absorption, without Defined Fractions 243 9.4.4 Parallel First-Order Absorption Processes, without Defined Fractions 245 9.4.5 Zero-Order Input into the Depot Compartment 246 9.4.6 Parent and Metabolite Model: Differential Equations 247 CHAPTER 10 PK/PD Models 250 10.1 Introduction 250 10.2 Implementation of PD Models in NONMEM 251 10.3 $PRED 252 10.3.1 Direct-Effect PK/PD Examples: PK Concentrations in the Dataset 253 10.3.2 Direct-Effect PK/PD Example: PK from Computed Concentrations 255 10.4 $PK 256 10.4.1 Specific ADVANs (ADVAN1–ADVAN4 and ADVAN10–ADVAN12) 256 10.4.2 General ADVANs (ADVAN5–ADVAN9 and ADVAN13) 257 10.4.3 PREDPP: Effect Compartment Link Model Example (PD in $ERROR) 257 10.4.4 PREDPP: Indirect Response Model Example: PD in $DES 259 10.5 Odd-Type Data: Analysis of Noncontinuous Data 261 10.6 PD Model Complexity 262 10.7 Communication of Results 263 CHAPTER 11 Simulation Basics 265 11.1 Introduction 265 11.2 The Simulation Plan 265 11.2.1 Simulation Components 266 11.2.2 The Input–Output Model 266 11.2.3 The Covariate Distribution Model 270 11.2.4 The Trial Execution Model 273 11.2.5 Replication of the Study 274 11.2.6 Analysis of the Simulated Data 275 11.2.7 Decision Making Using Simulations 275 11.3 Miscellaneous Other Simulation-Related Considerations 276 11.3.1 The Seed Value 276 11.3.2 Consideration of Parameter Uncertainty 277 11.3.3 Constraining Random Effects or Responses 278 CHAPTER 12 Quality Control 285 12.1 Introduction 285 12.2 QC of the Data Analysis Plan 285 12.3 Analysis Dataset Creation 286 12.3.1 Exploratory Data Analysis and Its Role in Dataset QC 287 12.3.2 QC in Data Collection 287 12.4 QC of Model Development 288 12.4.1 QC of NM-TRAN Control Streams 289 12.4.2 Model Diagnostic Plots and Model Evaluation Steps as QC 290 12.5 Documentation of QC Efforts 290 12.6 Summary 291 References 292 Index 293

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Book SynopsisPredator-prey interactions are ubiquitous, govern the flow of energy up trophic levels, and strongly influence the structure of ecological systems. They are typically quantified using the functional response - the relationship between a predator's foraging rate and the availability of food.Trade ReviewThis textbook provides a comprehensive review of the topic and explains the key equations and concepts. It will be a welcome resource for new and experienced researchers of a topic in which, despite its regular appearance in textbooks and decades of scientific debate decades, it remains difficult to reconcile theory with field ecology. * Conservation Biology *The book will be a welcome resource for new and experienced researchers of a topic in which, despite its regular appearance in textbooks and decades of scientific debate decades, it remains difficult to reconcile theory with field ecology. * Conservation Biology *This textbook provides a comprehensive review of the topic and explains the key equations and concepts. The book will be a welcome resource for new and experienced researchers of a topic in which, despite its regular appearance in textbooks and decades of scientific debate decades, it remains difficult to reconcile theory with field ecology. * Journal of Conservation Biology *The book is very much worth reading and should belong into the library of any Ecology & Evolution department or person interested in feeding ecology, trophic interactions, and consumer-resource dynamics. * Arne Schröder, Basic and Applied Ecology *The book is very much worth reading and should belong into the library of any Ecology & Evolution department or person interested in feeding ecology, trophic interactions, and consumer-resource dynamics. * Arne Schröder, Basic and Applied Ecology *Table of ContentsPrologue 1: Introduction 2: The Basics and Origin of Functional Responses Models 3: What Causes Variation in Functional Response Parameters 4: Population Dynamics and the Functional Response 5: Multi-Species Functional Responses 6: Selection on Functional Response Parameters 7: Optimal Foraging 8: Detecting Prey Preferences and Prey Switching 9: Origin of the Tpe III Functional Response 10: Statistical Issues in the Fitting of Functional Responses 11: Challenges for theFuture of Functional Response Research Epilogue

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Book SynopsisControl theory, an interdisciplinary topic within the study of dynamical systems, is an important but often overlooked part of a physicist's education. This is the first broad and complete treatment of the subject specifically tailored for physicists, spanning the basics to the most recent advances.Trade Review'Exceptionally well written, organized and presented, Control Theory for Physicists is an ideal and comprehensive volume that is unreservedly recommended as a curriculum textbook. While a core addition to college and university library Mathematical Physics & Calculus collections, it should be noted for students, academia, physicists, and non-specialist general readers with an interest in the subject …' Midwest Book Review'… will enhance appreciation of the limits of practical applications of physics, especially those associated with thermodynamics and information theory … Highly recommended.' E. Kincanon, Choice Connect'This is a rare example of a textbook that is concise yet clear, math dense yet very accessible, and rigorous yet beautifully written. The treatment throughout prioritizes first-principles descriptions, with an emphasis on not only when control works but also when it fails. It includes well-contextualized examples and well-formulated problems. It is ready for classroom use, with additional resources for instructors-such as a solution manual and associated Mathematica notebooks-available from the publisher. A 100-page supplement on background mathematics is also available on the publisher's website, which provides a comprehensive review of key mathematical topics. As already noted by Hugo Touchette in his back cover endorsement, this book may indeed lead more departments to include control theory in their curriculum.' Adilson E. Motter, Professor of Physics and Astronomy at Northwestern University, Illinois, IEEE Control Systems'… Together with information theory, control theory is the area of engineering that has the most fundamental lessons to teach physicists, and John Bechhoefer's recent textbook, Control Theory for Physicists, is an excellent place to start learning them … the pedagogical presentation of the material in the book perfectly complements its engaging subject matter.' Michael Hinczewski, The BiophysicistTable of ContentsPart I. Core Material: 1. Historical introduction; 2. Dynamical systems; 3. Frequency-domain control; 4. Time-domain control; 5. Discrete-time systems; 6. System identification; Part II. Advanced Ideas: 7. Optimal control; 8. Stochastic systems; 9. Robust control; 10. Adaptive control; 11. Nonlinear control; Part III. Special Topics: 12. Discrete-state systems; 13. Quantum control; 14. Networks and complex systems; 15. Limits to control.

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Book SynopsisThis book is about how climate science works and why you should absolutely trust some of its conclusions and absolutely distrust others. Climate change raises new, foundational challenges in science. It requires us to question what we know and how we know it. The subject is important for society but the science is young and history tells us that scientists can get things wrong before they get them right. How, then, can we judge what information is reliable and what is open to question? Stainforth goes to the heart of the climate change problem to answer this question. He describes the fundamental characteristics of climate change and shows how they undermine the application of traditional research methods, demanding new approaches to both scientific and societal questions. He argues for a rethinking of how we go about the study of climate change in the physical sciences, the social sciences, economics, and policy. The subject requires nothing less than a restructuring of academic reseaTrade ReviewClimate is, in some respects, highly predictable; yet, in other respects, highly unpredictable. But there is no contradiction. The resolution of this seeming paradox in Predicting Our Climate Future leads in turn to a vision for how humankind must respond to this most important problem of all time. * George Akerlof, Nobel Laureate in Economics, 2001 *A profound yet very accessible guide to climate science, highlighting the significant uncertainties without apology. This book explains clearly why doubt creates a greater and more urgent need to act now to build a better future. * Trevor Maynard, Executive Director of Systemic Risks, Cambridge Centre for Risk Studies *The immense complexity of the climate system raises deep questions about what science can usefully say about the future. David Stainforth navigates philosophical and mathematical questions that could hardly be of greater practical importance. He questions what it is reasonable to ask of climate scientists and his conclusions challenge the way in which science should be conducted in the future. * Jim Hall, Professor of Climate and Environmental Risk, University of Oxford *Is the science settled? Are climate models rubbish? Stainforth's book serves up nuanced answers to big questions in climate science, in an easy conversational style. * Cameron Hepburn, Professor of Environmental Economics, University of Oxford *A thoughtful exploration of the foundations and limitations of climate prediction that explains how its chaotic and probabilistic nature lead to deep uncertainty when assessing climate risk. * Ramalingam Saravanan, Professor of Atmospheric Sciences, Texas A&M University *Predicting Our Climate Future is an erudite and very personal reflection on climate change, the state of climate science, and their implications for the decisions society needs to take. It should be top of the reading list for scientists, practitioners and anyone who wants to truly comprehend the challenge of climate prediction. * Simon Dietz, Professor of Environmental Policy, London School of Economics and Political Science *A provocative contribution to the literature of climate change. * Kirkus *Predicting Our Climate Future is an ambitious exploration of a critical topic. It is a recommended read for climate scientists, especially those trying to model the future, for the researchers-in many disciplines-that are focused on understanding and forecasting the physical and human impacts of the coming climate changes, and for policy makers engaged in climate issues. * Steven Earle, New York Journal of Books *Intelligent, accessible, well reasoned and working very hard to get it's teeth into a complex but vitally important issue. * Irish Tech News *Fascinating...[there is a] a refreshing honesty [in Stainforth's writing] about the limitations we have with certain kinds of prediction. * Brian Clegg, Popular Science *Stainforth is good at explaining the complexities [of climate modelling], leavening the highly technical bits with ... lots of relatable real-world analogies. * Geordie Torr, The Geographical *Table of ContentsSection 1 Chapter 1: The obvious and the obscure Chapter 2: A problem of prediction Chapter 3: Going beyond what we've seen Chapter 4: The one-shot bet. Chapter 5: From chaos to pandemonium Chapter 6: The curse of bigger and better computers Chapter 7: Talking at cross purposes Chapter 8: Not just of academic interest Section 2 Challenge 1: How to balance justified arrogance with essential humility. Chapter 9 - Stepping up to the task of prediction Chapter 10 The Times They Are A Changin' Chapter 11 Starting from scratch Chapter 12 Are scientists being asked to answer impossible questions? Challenge 2: Tying down what we mean by climate and climate change. Chapter 13 The essence of climate Chapter 14 A Walk in Three Dimensions Chapter 15 A walk in three dimensions over a two dimensional sea Challenge 3: When is a study with a climate model a study of climate change? Chapter 16 Climate change in climate models Challenge 4: How can we measure what climate is now and how it has changed? Chapter 17 Measuring climate change Challenge 5: How can we relate what happens in a model to what will happen in reality? Chapter 18 - Can climate models be realistic? Chapter 19 More models, better information? Chapter 20 How bad is too bad? Challenge 6: How can we use today's climate science well? Chapter 21 - What we do with what we've got Challenge 7: Getting a grip on the scale of future changes in climate? Chapter 22 - Stuff of the Genesis myth Chapter 23 Things ... can only get hotter Challenge 8: How can we use the information we have, or could have, to design a future that is better than it would otherwise be? Chapter 24 - Making it personal Chapter 25 - Where physics and economics meet. Challenge 9: How can we build physical and social science that is up to the task of informing society about what matters for society? Chapter 26 - Controlling factors. Chapter 27 - Beyond comprehension? No, just new challenges for human intellect.

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Book SynopsisThis book addresses the state of the art of reduced order methods for modelling and computational reduction of complex parametrised systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in various fields.Consisting of four contributions presented at the CIME summer school, the book presents several points of view and techniques to solve demanding problems of increasing complexity. The focus is on theoretical investigation and applicative algorithm development for reduction in the complexity – the dimension, the degrees of freedom, the data – arising in these models.The book is addressed to graduate students, young researchers and people interested in the field. It is a good companion for graduate/doctoral classes.Table of Contents- 1. The Reduced Basis Method in Space and Time: Challenges, Limits and Perspectives. - 2. Inverse Problems: A Deterministic Approach Using Physics-Based Reduced Models. - 3. Model Order Reduction for Optimal Control Problems. - 4. Machine Learning Methods for Reduced Order Modeling.

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Book SynopsisProviding a detailed and pedagogical account of the rapidly-growing field of computational statistical physics, this book covers both the theoretical foundations of equilibrium and non-equilibrium statistical physics, and also modern, computational applications such as percolation, random walks, magnetic systems, machine learning dynamics, and spreading processes on complex networks. A detailed discussion of molecular dynamics simulations is also included, a topic of great importance in biophysics and physical chemistry. The accessible and self-contained approach adopted by the authors makes this book suitable for teaching courses at graduate level, and numerous worked examples and end of chapter problems allow students to test their progress and understanding.Table of ContentsPreface; Part I. Stochastic Methods: 1. Random Numbers; 2. Random-Geometrical Models; 3. Equilibrium Systems; 4. Monte-Carlo Methods; 5. Phase Transitions; 6. Cluster Algorithms; 7. Histogram Methods; 8. Renormalization Group; 9. Learning and Optimizing; 10. Parallelization; 11. Non-Equilibrium Systems; Part II. Molecular Dynamics: 12. Basic Molecular Dynamics; 13. Optimizing Molecular Dynamics; 14. Dynamics of Composed Particles; 15. Long-Range Potentials; 16. Canonical Ensemble; 17. Inelastic Collisions in Molecular Dynamics; 18. Event-Driven Molecular Dynamics; 19. Non-Spherical Particles; 20. Contact Dynamics; 21. Discrete Fluid Models; 22. Ab-Initio Simulations; References; Index.

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Book SynopsisThis volume provides a setting for a dialogue about ethics, when using mathematical models, and shows the need to continue and define a vocabulary for exploring ethical concerns. The text should act as a clear and strong code of ethics for those having to make ethical decisions.Trade ReviewThis collection of essays makes very interesting reading, reminding us of the ubiquity of modeling in our lives today and of the ethical considerations required of modelers and statisticians. Journal of the American Statistical Association T.G. Groleau, Bethel College, Indiana In summary, Ethics in Modeling does a good job of unveiling the ethical aspects of modeling and covers ethics from many angles. While most chapters would be beyond the grasp of the average undergraduate, they could make good background for a graduate seminar. The book is thought provoking and, at times, deeply philosophical. Anyone doing active modeling research would benefit by taking some time to dwell on these issues.Table of ContentsAcknowledgements. Introduction. How Do the Construction and/or Interpretation of Models Affect Our Decisions? Uses of modeling in science and society (J. Allison et al.). An epistemological view of decision aid technology with emphasis on expert systems (H.D. Carrier, W.A. Wallace). Models in the public sector: success, failure, and ethical behavior (J.M. Mulvey). How Do Values Become Incorporated in Models? Rhetoric and rigor in macroeconomic models: populist and orthodox swings in Latin America (P.D. McNelis). Ethical and modeling considerations in correcting the results of the 1990 decennial census (S.E. Fienberg). The role of models in managerial decision making, never say the model says (V.P. Barabba). What Are the Ethical Responsibilities of Model Builders? From model building to risk management: evolving standards of professional responsibility (N.P. Ross, S. Harris). On model building (J.D.C. Little). Morality and models (R.O. Mason). One-sided practice, can we do better? (J. Rosenhead). Where Do We Go from Here? Ethical concerns and ethical answers (S.I. Gass). Responsible policy modeling (W.E. Walker). Society's role in the ethics of modeling (E.H. Leet, W.A. Wallace). Appendix A: Authors. Appendix B: Ethics in Modeling Workshop agenda. Appendix C: Ethics in Modeling Workshop Participants. Author Index. Subject Index.

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Book SynopsisIntended for advanced undergraduate and beginning graduate courses on Modeling, this book introduces a variety of mathematical models for biological systems, and presents the mathematical theory and techniques useful in analyzing those models. Its material is organized according to the mathematical theory rather than the biological application.Table of ContentsPreface xi 1 LINEAR DIFFERENCE EQUATIONS, THEORY, AND EXAMPLES 1 1.1 Introduction 1 1.2 Basic Definitions and Notation 2 1.3 First-Order Equations 6 1.4 Second-Order and Higher-Order Equations 8 1.5 First-Order Linear Systems 14 1.6 An Example: Leslie’s Age-Structured Model 18 1.7 Properties of the Leslie Matrix 20 1.8 Exercises for Chapter 1 28 1.9 References for Chapter 1 33 1.10 Appendix for Chapter 1 34 1.10.1 Maple Program:Turtle Model 34 1.10.2 MATLAB® Program:Turtle Model 34 2 NONLINEAR DIFFERENCE EQUATIONS, THEORY, AND EXAMPLES 36 2.1 Introduction 36 2.2 Basic Definitions and Notation 37 2.3 Local Stability in First-Order Equations 40 2.4 Cobwebbing Method for First-Order Equations 45 2.5 Global Stability in First-Order Equations 46 2.6 The Approximate Logistic Equation 52 2.7 Bifurcation Theory 55 2.7.1 Types of Bifurcations 56 2.7.2 Liapunov Exponents 60 2.8 Stability in First-Order Systems 62 2.9 Jury Conditions 67 2.10 An Example: Epidemic Model 69 2.11 Delay Difference Equations 73 2.12 Exercises for Chapter 2 76 2.13 References for Chapter 2 82 2.14 Appendix for Chapter 2 84 2.14.1 Proof of Theorem 2.1 84 2.14.2 A Definition of Chaos 86 2.14.3 Jury Conditions (Schur-Cohn Criteria) 86 2.14.4 Liapunov Exponents for Systems of Difference Equations 87 2.14.5 MATLAB Program: SIR Epidemic Model 88 3 BIOLOGICAL APPLICATIONS OF DIFFERENCE EQUATIONS 89 3.1 Introduction 89 3.2 Population Models 90 3.3 Nicholson-Bailey Model 92 3.4 Other Host-Parasitoid Models 96 3.5 Host-Parasite Model 98 3.6 Predator-Prey Model 99 3.7 Population Genetics Models 103 3.8 Nonlinear Structured Models 110 3.8.1 Density-Dependent Leslie Matrix Models 110 3.8.2 Structured Model for Flour Beetle Populations 116 3.8.3 Structured Model for the Northern Spotted Owl 118 3.8.4 Two-Sex Model 121 3.9 Measles Model with Vaccination 123 3.10 Exercises for Chapter 3 127 3.11 References for Chapter 3 134 3.12 Appendix for Chapter 3 138 3.12.1 Maple Program: Nicholson-Bailey Model 138 3.12.2 Whooping Crane Data 138 3.12.3 Waterfowl Data 139 4 LINEAR DIFFERENTIAL EQUATIONS: THEORY AND EXAMPLES 141 4.1 Introduction 141 4.2 Basic Definitions and Notation 142 4.3 First-Order Linear Differential Equations 144 4.4 Higher-Order Linear Differential Equations 145 4.4.1 Constant Coefficients 146 4.5 Routh-Hurwitz Criteria 150 4.6 Converting Higher-Order Equations to First-OrderSystems 152 4.7 First-Order Linear Systems 154 4.7.1 Constant Coefficients 155 4.8 Phase-Plane Analysis 157 4.9 Gershgorin’s Theorem 162 4.10 An Example: Pharmacokinetics Model 163 4.11 Discrete and Continuous Time Delays 165 4.12 Exercises for Chapter 4 169 4.13 References for Chapter 4 172 4.14 Appendix for Chapter 4 173 4.14.1 Exponential of a Matrix 173 4.14.2 Maple Program: Pharmacokinetics Model 175 5 NONLINEAR ORDINARY DIFFERENTIAL EQUATIONS: THEORY AND EXAMPLES 176 5.1 Introduction 176 5.2 Basic Definitions and Notation 177 5.3 Local Stability in First-Order Equations 180 5.3.1 Application to Population Growth Models 181 5.4 Phase Line Diagrams 184 5.5 Local Stability in First-Order Systems 186 5.6 Phase Plane Analysis 191 5.7 Periodic Solutions 194 5.7.1 Poincaré-Bendixson Theorem 194 5.7.2 Bendixson’s and Dulac’s Criteria 197 5.8 Bifurcations 199 5.8.1 First-Order Equations 200 5.8.2 Hopf Bifurcation Theorem 201 5.9 Delay Logistic Equation 204 5.10 Stability Using Qualitative Matrix Stability 211 5.11 Global Stability and Liapunov Functions 216 5.12 Persistence and Extinction Theory 221 5.13 Exercises for Chapter 5 224 5.14 References for Chapter 5 232 5.15 Appendix for Chapter 5 234 5.15.1 Subcritical and Supercritical Hopf Bifurcations 234 5.15.2 Strong Delay Kernel 235 6 BIOLOGICAL APPLICATIONS OF DIFFERENTIAL EQUATIONS 237 6.1 Introduction 237 6.2 Harvesting a Single Population 238 6.3 Predator-Prey Models 240 6.4 Competition Models 248 6.4.1 Two Species 248 6.4.2 Three Species 250 6.5 Spruce Budworm Model 254 6.6 Metapopulation and Patch Models 260 6.7 Chemostat Model 263 6.7.1 Michaelis-Menten Kinetics 263 6.7.2 Bacterial Growth in a Chemostat 266 6.8 Epidemic Models 271 6.8.1 SI, SIS, and SIR Epidemic Models 271 6.8.2 Cellular Dynamics of HIV 276 6.9 Excitable Systems 279 6.9.1 Van der Pol Equation 279 6.9.2 Hodgkin-Huxley and FitzHugh-Nagumo Models 280 6.10 Exercises for Chapter 6 283 6.11 References for Chapter 6 292 6.12 Appendix for Chapter 6 296 6.12.1 Lynx and Fox Data 296 6.12.2 Extinction in Metapopulation Models 296 7 PARTIAL DIFFERENTIAL EQUATIONS: THEORY, EXAMPLES, AND APPLICATIONS 299 7.1 Introduction 299 7.2 Continuous Age-Structured Model 300 7.2.1 Method of Characteristics 302 7.2.2 Analysis of the Continuous Age-Structured Model 306 7.3 Reaction-Diffusion Equations 309 7.4 Equilibrium and Traveling Wave Solutions 316 7.5 Critical Patch Size 319 7.6 Spread of Genes and Traveling Waves 321 7.7 Pattern Formation 325 7.8 Integrodifference Equations 330 7.9 Exercises for Chapter 7 331 7.10 References for Chapter 7 336 Index 339

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Book SynopsisA bold, visionary, and mind-bending exploration of how the geometry of chaos can explain our uncertain world - from weather and pandemics to quantum physics and free willCovering a breathtaking range of topics - from climate change to the foundations of quantum physics, from economic modelling to conflict prediction, from free will to consciousness and spirituality - The Primacy of Doubt takes us on a unique journey through the science of uncertainty. A key theme that unifies these seemingly unconnected topics is the geometry of chaos: the beautiful and profound fractal structures that lie at the heart of much of modern mathematics. Royal Society Research Professor Tim Palmer shows us how the geometry of chaos not only provides the means to predict the world around us, it suggests new insights into some of the most astonishing aspects of our universe and ourselves. This important and timely book helps the reader makes sense of uncertainty in a rapidly changing world.Trade Reviewimportant book * Andrew Robinson, Nature *The Primacy of Doubt also contains very informative explanations as to the application of chaos theory in climate and meteorological models, and why meteorologists failed to predict southern Britain's 1987 hurricane. To my mind this were probably the book's strongest areas and are 'must reads' for those with an interest in climate forecasting. * Jonathan Cowie, SF2 Concatenation *Quite possibly the best popular science book I've ever read... The Primacy of Doubt is like getting off one of those exciting roller coaster rides, when your immediate inclination is to think 'I want to do that again, but I'll have a bit of a break first.' I will be reading this book again, without doubt. Remarkable. * Brian Clegg, Popular Science *delightful and substantive * William Hooke, Living on the Real World *The Primacy of Doubt provides a remarkably broad-ranging account of uncertainty in physics, in all its various aspects. I strongly recommend this highly thought-provoking book. * Roger Penrose, OM, FRS, winner of the 2020 Nobel Prize in Physics *Tim Palmer is a scientific polymath. It's hard to think of anyone else who could have written so authoritatively—and so accessibly—on themes extending from quantum gravity to climate modelling. This fascinating and important book offers some profoundly original speculations on conceptual linkages across different sciences. * Lord Martin Rees, Astronomer Royal of the United Kingdom *The Primacy of Doubt is an important book by one of the pioneers of dynamical weather prediction, indispensable for daily life. * Suki Manabe, winner of the 2021 Nobel Prize in Physics *In a whirlwind of a book that's partly scientific autobiography and partly the manifest of a visionary, Tim Palmer masterfillly weaves together climate change and quantum mechanics into one coherent whole. Using uncertainty as a unifring principle, Palmer puts forward new perspectives on old problems. A revolutionary thinker way ahead of his time. * Sabine Hossenfelder, author of Lost in Math *An exploration of the amorphous concept of uncertainty... [an] informative, ingenious book. * Kirkus Reviews *Physicist Palmer delivers a challenging but rewarding look at how uncertainty helps scientists make sense of the world... Despite the complexity of his arguments, the author succeeds at bringing complicated theories within reach of those who have a basic familiarity with physics. Science-minded readers, take note. * Publishers Weekly *Provocative... useful for scientists and non-scientists alike * Jessica Flack, Physics World *Table of ContentsPreface 1: The Primacy of Doubt DS From Two Perspectives Part I: The Science of Uncertainty and the Geometry of Chaos 2: Chaos, Chaos Everywhere 3: The Geometry of Chaos 4: Noisy, Million-Dollar Butterflies 5: Quantum Uncertainty DS Determinism Lost? Part II: The Science of Uncertainty to Predict Our Chaotic World 6: The Two Roads to Monte Carlo 7: Climate Change: Catastrophe or Just Lukewarm? 8: Pandemics 9: Financial Crashes 10: Deadly Conflict and the Digital Ensemble of Spaceship Earth 11: Decisions! Decisions! Part III: The Science of Uncertainty to Understand Our Chaotic World 12: Quantum Uncertainty: Determinism Regained? 13: Noisy Billion-Dollar Brains 14: Free Will, Consciousness and Theology Acknowledgements Bibliography

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Book SynopsisA concise, accessible introduction to the principal ideas, methods, and underlying statistical concepts for understanding and applying evolutionary quantitative genetics. It includes a broad taxonomic range of examples - human, animal, and plant; both model organisms and wild populations.Table of ContentsIntroduction 1: Selection on a Single Trait 2: Selection on Multiple Traits 3: The Selection Surface and Adaptive Landscape for a Single Trait 4: The Selection Surface and Adaptive Landscape for Multiple Traits 5: Inheritance of a Single Trait 6: Inheritance of Multiple Traits 7: Modularity, Performance, and Functional Complexes 8: Drift of a Single, Neutral Trait 9: Drift of Multiple, Neutral Traits 10: Response of a Single Trait to Selection 11: Response of Multiple Traits to Selection 12: Evolution of a Single Trait on a Stationary Adaptive Landscape 13: Evolution of Multiple Traits on a Stationary Adaptive Landscape 14: Trait Evolution on Dynamic Adaptive Landscapes 15: Evolution of Genetic Variance 16: Evolution of the G-Matrix on a Stationary Adaptive Landscape 17: Evolution of the G-Matrix on Dynamic Adaptive Landscapes 18: Evolution Along Selective Lines of Least Resistance 19: Speciation and Extinction of Lineages 20: Coevolution of Species with Trait-Based Interactions 21: Coevolution of Species with Density-Dependent Interactions 22: From Evolutionary Process to Pattern: A Synthesis

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Book SynopsisThis novel textbook addresses the shortcomings of current competition theory and suggests a more useful approach that can provide a basis for future models that have far greater predictive ability in both ecology and evolution.Trade ReviewThis book offers readers a compelling introduction to these complexities. * Mark A. McPeek, Biological Sciences, Dartmouth College, Hanover, New Hampshire, The Quarterly Review of Biology *

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Book SynopsisThis novel and accessible textbook focuses on the intimate interplay between ecological and evolutionary processes as populations spread through time and space. It provides both a survey of the field a story about the history and development of our understanding as well as a synthesis of the new developments.

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Book SynopsisThe idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables. The use of graphical models in statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides the first comprehensive and authoritative account of the theory of graphical models and is written by a leading expert in the field. It contains the fundamental graph theory required and a thorough study of Markov properties associated with various type of graphs. The statistical theory of log-linear and graphical models for contingency tables, covariance selection models, and graphical models with mixed discrete-continous variables in developed detail. Special topics, such as the application of graphical models to probabilistic expert systems, are described briefly, Trade Review...this is an excellent reference book for those who are interested in studying the mathematical theory for graphical models. * Short Book Reviews, vol. 18, no. 1, April 1998 *Table of ContentsIntroduction ; 1. Graphs and Hypergraphs ; 2. Conditional Independence and Markov Properties ; 3. Contingency Tables ; 4. Multivariate Normal Models ; 5. Models for Mixed Data ; 6. Further topics ; A Various Prerequisites ; B Linear Algebra and Random Vectors ; C The Multivariate Distribution ; D Exponential Models

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Book SynopsisFinancial Asset Pricing Theory offers a comprehensive overview of the classic and the current research in theoretical asset pricing. Asset pricing is developed around the concept of a state-price deflator which relates the price of any asset to its future (risky) dividends and thus incorporates how to adjust for both time and risk in asset valuation. The willingness of any utility-maximizing investor to shift consumption over time defines a state-price deflator which provides a link between optimal consumption and asset prices that leads to the Consumption-based Capital Asset Pricing Model (CCAPM). A simple version of the CCAPM cannot explain various stylized asset pricing facts, but these asset pricing ''puzzles'' can be resolved by a number of recent extensions involving habit formation, recursive utility, multiple consumption goods, and long-run consumption risks. Other valuation techniques and modelling approaches (such as factor models, term structure models, risk-neutral valuation, and option pricing models) are explained and related to state-price deflators. The book will serve as a textbook for an advanced course in theoretical financial economics in a PhD or a quantitative Master of Science program. It will also be a useful reference book for researchers and finance professionals. The presentation in the book balances formal mathematical modelling and economic intuition and understanding. Both discrete-time and continuous-time models are covered. The necessary concepts and techniques concerning stochastic processes are carefully explained in a separate chapter so that only limited previous exposure to dynamic finance models is required.Trade ReviewThis monograph provides a consistent and comprehensive presentation of the classical asset pricing paradigm, from the basics of the theory to the latest developments in the field. The reader's task is simplified by the consistent notation and the integrated conceptual framework that is employed; his technical facility improved by the extensive proofs of the main results that are offered; and his curiosity piqued by the extensive references to the empirical literature. The expert will find it a convenient reference and the student will find it an invaluable guide. * Michael J. Brennan, Professor of Finance at Anderson School, University of California Los Angeles, at Manchester Business School, and at King Abdulaziz University, Jeddah *Munk takes a completely fresh and well organized approach to communicating the key concepts and techniques of modern asset pricing theory. His treatment is clear, accessible, rigorously unified around the notion of state pricing, and encompasses the latest model specifications. He has set the new standard for doctoral-level courses on this subject. * Darrell Duffie, Dean Witter Distinguished Professor of Finance, at the Graduate School of Business, Stanford University *Financial Asset Pricing Theory is a rigorous, yet eminently accessible, textbook at the frontier of modern asset pricing theory with applications in portfolio management, the term structure of interest rates, and derivatives, and a nice selection of problem sets. Claus Munk's textbook is my top choice as a comprehensive and intuitive textbook for an introductory or advanced PhD course on asset pricing theory. * George M. Constantinides, Leo Melamed Professor of Finance, The University of Chicago, Booth School of Business *Table of ContentsPreface ; 1. Introduction and Overview ; 2. Uncertainty, Information, and Stochastic Processes ; 3. Portfolios, Arbitrage, and Market Completeness ; 4. State Prices ; 5. Preferences ; 6. Individual Optimality ; 7. Market Equilibrium ; 8. Basic Consumption-Based Asset Pricing ; 9. Advanced Consumption-Based Asset Pricing ; 10. Factor Models ; 11. The Economics of the Term Structure of Interest Rates ; 12. Risk-Adjusted Probabilities ; 13. Derivatives ; Appendix A. A Review of Basic Probability Concepts ; Appendix B. Results on the Lognormal Distribution ; Appendix C. Results from Linear Algebra

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Book SynopsisThis textbook provides an introduction to the world of numerical modeling in the physical sciences, focusing more specifically on earth and planetary sciences. It is designed to lead the reader through the process of defining the mathematical or physical model of interest and applying numerical methods to approximate and explore the solutions to these models, while also providing a quantitative assessment of the limitations, performance and quality of these approximations. The book is designed to provide a self-contained reference by including the mathematical foundations required to understand the models and their convergence. It includes a detailed discussion of models for ordinary systems of equation and partial differential equations, with pseudo-codes detailing the solution procedure. Examples are drawn from the fields of earth and planetary sciences, including, geochemical box models, non-linear ordinary differential equations describing the evolution of subvolcanic magma chambers, the mass conservation of cosmogenic nuclides in soils, diffusion in minerals, the hillslope equation, the advection-diffusion and wave equations and the shallow water equations. Featuring numerous examples drawn from earth and planetary sciences, the content of this book has been used by the author to teach numerical methods classes at the undergraduate and graduate levels over several years, and will provide an excellent resources for teachers and learners in this area.

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Book SynopsisThe field of molecular and genomic evolution has been catalysed by the ever increasing availability of high throughput data such as transcriptome evolution, genotype-phenotype evolution, and genetic robustness. However, there is also an urgent requirement for the emergence of new paradigms (universally accepted scientific frameworks) supported by conceptual breakthroughs, since there is now widespread agreement that genome evolution research should be far more than a static pattern characterized by some well-known arguments and yet more big data for testing or extension. Furthermore, while the internet has made a vast body of literature and data widely accessible, researchers are increasingly facing significant challenges in how to select from this huge reserve appropriately and systematically. Statistical Analysis of Molecular and Genomic Evolution sets out to provide a solution to the most frequently asked question by next-generation young researchers in the area of evolutionary genomics: What is the knowledge that is essential for moving the research forward and where can it be found? Although the book incorporates the latest research foci, it is written at the simplest mathematical level whilst sophisticated enough to provide a deep understanding of current principles and methods. Technical issues are described only briefly, mathematical derivations are kept to a minimum, and it is structured and presented in a way that encourages its use as a graduate textbook. Mindful of the steep learning curve that some biologist readers may face, online appendices review basic mathematical and statistical concepts used in the book, and provide further examples and practical exercises.This is an advanced textbook suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, bioinformatics, mathematics, and statistics.

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Book SynopsisAn accessible and interdisciplinary introduction to the applications of statistical mechanics across the sciences. The book contains a discussion of the methods of statistical physics and includes mathematical explanations alongside guidance to enable the reader to translate theory into practice.Trade ReviewThis book has a good mix of interesting topics and shows the breadth of application of the statistical mechanics concepts. * Robert M. Ziff, University of Michigan *The book's subject is one which is of great interest and impacts many areas both within and outside physics. I am not aware of any other textbook which includes engaging mathematical content alongside a wide range of accessible applications, so this text has the potential to appeal to both the lay person and the technical expert. * Peter Richmond, Trinity College Dublin *Explores statistical mechanics at its glorious best, in the form of practical applications of collective behaviors found in the real world. Schulman is refreshingly honest in his approach, helping to stake out the frontiers of the field, posing problems that will inspire and direct future generations of scientists. * Daniel Sheehan, University of San Diego *I think that the book's collection of topics and its unique style make it a useful addition to the more standard textbook offering. Moreover, given the more colloquial style of the book, I imagine that it may be suitable for an audience that is interested in the physics of emergence and complexity that goes beyond the popular science literature. * Stefan Kirchner, Zhejiang University *I expect that anyone interested in complex systems and who has the requisite knowledge of elementary calculus and linear algebra will find When Things Grow Many to be a rewarding read. * Robert Deegan, University of Michigan, Physics Today *I enjoyed reading When Things Grow Many and learned something new from each chapter. Schulman writes in a conversational style, and he peppers the book with jokes and opinions. Even though he intimates that he doesn't have all the answers, his fun, inviting tone will make readers want to find out if he does. * Robert Deegan, Physics Today *This book ensures that all readers can grasp the fundamental principles and applications of physics, making it an excellent educational tool for a wide range of students. * Miguel A. F. Sanjuán, Contemporary Physics *This book ensures that all readers can grasp the fundamental principles and applications of physics, making it an excellent educational tool for a wide range of students. * Miguel A. F. Sanjuán, Contemporary Physics *Table of Contents1: Introduction 2: Ideal Gas 3: Rubber Bands 4: Percolitis 5: Ferromagnetism 6: Maximum Entropy Methods 7: Power Laws 8: Universality, Renormalization and Critical Phenomena 9: Social Sciences 10: Biological Sciences 11: Physical Sciences Free

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Book SynopsisPublished in 2002, the first edition of Geostatistical Reservoir Modeling brought the practice of petroleum geostatistics into a coherent framework, focusing on tools, techniques, examples, and guidance. It emphasized the interaction between geophysicists, geologists, and engineers, and was received well by professionals, academics, and both graduate and undergraduate students.In this revised second edition, Deutsch collaborates with co-author Michael Pyrcz to provide a full update on the latest tools, methods, practice, and research in the field of petroleum Geostatistics. Key geostatistical concepts such as integration of production data, scale-up, and cosimulation receive greater attention, and new topics like model checking, multiple point simulation, and production data integration are included in detail. Geostatistical methods are extensively illustrated through enhanced schematics, work flows and examples. A greater number of examples also are included, such as the integration oTable of Contents1. Introduction ; 2. Modeling Principles ; 3. Modeling Prerequisites ; 4. Modeling Methods ; 5. Model Applications ; 6. Special Topics ; Glossary and Notation ; Bibliography ; Index

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Book SynopsisIn this work, Martin H. Krieger seeks to show what physicists really do behind the nearly impenetrable cloud of mathematical models they use as research tools. He argues that the technical details of these complex calculations also reveal key aspects of the physical properties they model.Table of ContentsList of Figures Preface 1: Modeling the Constitutions of Matter 2: Analysis: The Stability of Bulk Matter 3: Mathematics: Infinite Volume Limits and Thermodynamics 4: Formalism: Constituting Bulk Matter: Solutions to the Ising Model, and Duality in Those Solutions 5: Analysis: Generic, Formal, Model-Independent Accounts of the Constitution of Matter as Philosophical 6: Formalism: Technical Devices Doing the Work of Physics 7: Physics and Mathematics: Finding the Right Mechanism and Choosing the Right Functions 8: The Physics and the Mathematics Appendix: Two Papers by Lars Onsager A: Electrostatic Interaction of Molecules (1939) B: Crystal Statistics, Part I: A Two-Dimensional Model with an Order-Disorder Transition (1944) Notes References Index

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Book SynopsisMathematical models have become central to the study of social evolution, both in biology and the social sciences. A primer on behavioral modeling that includes both mathematics and evolutionary theory, this book aims to make the student and professional researcher in biology and the social sciences fully conversant in the language of the field.Trade Review"Evolutionary arguments are increasingly used as explanations in a wide range of human sciences - psychology, economics, anthropology - as well as in biology itself. However, these arguments are frequently employed on the basis of a secondhand understanding of the principles by which they are derived. This is the first book to provide a thorough but accessible grounding in the methods underlying the major topics in the evolution of social behavior. It should become required study for graduate students in evolution and human behavior." - Daniel Nettle, Newcastle University"

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Book SynopsisTrade Review“Beautifully bringing together historical and contemporary research on representations in science with themes from aesthetics and the philosophy of art, Suárez’s book is an outstanding interdisciplinary contribution to the philosophy of science. It is essential reading for anyone interested in modeling practices, their connections with the arts, and what this insightful combination of science, art, and practice might bring to the epistemology of science.” -- Chiara Ambrosio, University College London“Suárez has been a leading voice in the philosophy of modeling for the last two decades. This book is a wonderfully clear and compelling presentation of his ‘inferentialist theory of representation.’ The book will be a central resource for advanced undergraduate and graduate students, and required reading for every philosopher of science.” -- Martin Kusch, University of Vienna“Suárez has written a brilliant account of the inferential conception of scientific representation, its historical roots, and its application to contemporary scientific modeling. What stands out is his deflationist approach toward metaphysics, the streamlined account in terms of representational force and inferential capacity, and the connection to the phenomenology of artistic perception. A magnificent work.” -- Bas C. van Fraassen, Princeton University“Inference and Representation makes a strong case for an inferential conception of scientific modeling. It argues that the effectiveness of a model lies in its providing an orientation that facilitates fruitful scientific reasoning. It is a valuable contribution to the literature on modeling.” -- Catherine Z. Elgin, Harvard University“This much-anticipated book is the culmination of over twenty years of pioneering work by Suárez. It is a must-read for anyone wishing to think carefully about models and representations in science. Suárez gives a careful, insightful, and comprehensive exposition and defence of his inferential conception of representation, and he now develops it in an expressly pragmatist direction with a helpful focus on the uses of models. What emerges is a compelling deflationary account of ‘representation without metaphysics,’ engaging fully with the complex realities of inferential practices. Suárez argues that common notions of representation based on similarity or isomorphism are ill-fitting and inadequate, and shows how the activity of representation pervades all sorts of scientific practices. His discussion is clear and systematic throughout, and successfully combines philosophical acuity and historical awareness. In the course of presenting his own position he also gives a fair, critical summing-up and evaluation of the considerable existing literature on models and representation. This landmark work should appeal to philosophers, historians of science and practicing scientists alike.” -- Hasok Chang, University of Cambridge“During the past quarter-century, philosophers of science have come to appreciate the importance of models and modeling practices in the sciences. Suárez has been one of the pioneers in this work, specifically in investigating how models represent aspects of the world. The present book is the culmination of insights accumulated over more than two decades. It provides a convincing account of representation, one emphasizing the uses to which models are put and the inferences they allow. Suárez develops his views with welcome precision, focuses on an admirably wide range of types of models, and offers numerous insights about the historical development of modeling. His final two chapters explore the notion of representation more broadly, with a lucid and well-informed discussion of representation in visual art, and draw out the implications for several large issues in the philosophy of science. This book is an outstanding contribution to the field.” -- Philip Kitcher, Columbia UniversityTable of ContentsPreface and Acknowledgments 1 Introducing Scientific Representation Part I Modeling 2 The Modeling Attitude: A Genealogy 3 Models and Their Uses Part II Representation 4 Theories of Representation 5 Against Substance 6 Scientific Theories and Deflationary Representation 7 Representation as Inference Part III Implications 8 Lessons from the Philosophy of Art 9 Scientific Epistemology Transformed Notes References Index

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Book SynopsisThe first comprehensive defense of an inferential conception of scientific representation with applications to art and epistemology. Mauricio Suárez develops a conception of representation that delivers a compelling account of modeling practice. He begins by discussing the history and methodology of model building, charting the emergence of what he calls the modeling attitude, a nineteenth-century and fin de siècle development. Prominent cases of models, both historical and contemporary, are used as benchmarks for the accounts of representation considered throughout the book. After arguing against reductive naturalist theories of scientific representation, Suárez sets out his own account: a case for pluralism regarding the means of representation and minimalism regarding its constituents. He shows that scientists employ a variety of modeling relations in their representational practicewhich helps them to assess the accuracy of their representationswhile demonstrating that there is Trade Review“Beautifully bringing together historical and contemporary research on representations in science with themes from aesthetics and the philosophy of art, Suárez’s book is an outstanding interdisciplinary contribution to the philosophy of science. It is essential reading for anyone interested in modeling practices, their connections with the arts, and what this insightful combination of science, art, and practice might bring to the epistemology of science.” -- Chiara Ambrosio, University College London“Suárez has been a leading voice in the philosophy of modeling for the last two decades. This book is a wonderfully clear and compelling presentation of his ‘inferentialist theory of representation.’ The book will be a central resource for advanced undergraduate and graduate students, and required reading for every philosopher of science.” -- Martin Kusch, University of Vienna“Suárez has written a brilliant account of the inferential conception of scientific representation, its historical roots, and its application to contemporary scientific modeling. What stands out is his deflationist approach toward metaphysics, the streamlined account in terms of representational force and inferential capacity, and the connection to the phenomenology of artistic perception. A magnificent work.” -- Bas C. van Fraassen, Princeton University“Inference and Representation makes a strong case for an inferential conception of scientific modeling. It argues that the effectiveness of a model lies in its providing an orientation that facilitates fruitful scientific reasoning. It is a valuable contribution to the literature on modeling.” -- Catherine Z. Elgin, Harvard University“This much-anticipated book is the culmination of over twenty years of pioneering work by Suárez. It is a must-read for anyone wishing to think carefully about models and representations in science. Suárez gives a careful, insightful, and comprehensive exposition and defence of his inferential conception of representation, and he now develops it in an expressly pragmatist direction with a helpful focus on the uses of models. What emerges is a compelling deflationary account of ‘representation without metaphysics,’ engaging fully with the complex realities of inferential practices. Suárez argues that common notions of representation based on similarity or isomorphism are ill-fitting and inadequate, and shows how the activity of representation pervades all sorts of scientific practices. His discussion is clear and systematic throughout, and successfully combines philosophical acuity and historical awareness. In the course of presenting his own position he also gives a fair, critical summing-up and evaluation of the considerable existing literature on models and representation. This landmark work should appeal to philosophers, historians of science and practicing scientists alike.” -- Hasok Chang, University of Cambridge“During the past quarter-century, philosophers of science have come to appreciate the importance of models and modeling practices in the sciences. Suárez has been one of the pioneers in this work, specifically in investigating how models represent aspects of the world. The present book is the culmination of insights accumulated over more than two decades. It provides a convincing account of representation, one emphasizing the uses to which models are put and the inferences they allow. Suárez develops his views with welcome precision, focuses on an admirably wide range of types of models, and offers numerous insights about the historical development of modeling. His final two chapters explore the notion of representation more broadly, with a lucid and well-informed discussion of representation in visual art, and draw out the implications for several large issues in the philosophy of science. This book is an outstanding contribution to the field.” -- Philip Kitcher, Columbia UniversityTable of ContentsPreface and Acknowledgments 1 Introducing Scientific Representation Part I Modeling 2 The Modeling Attitude: A Genealogy 3 Models and Their Uses Part II Representation 4 Theories of Representation 5 Against Substance 6 Scientific Theories and Deflationary Representation 7 Representation as Inference Part III Implications 8 Lessons from the Philosophy of Art 9 Scientific Epistemology Transformed Notes References Index

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Book SynopsisPHIL PICTON is a Reader in Engineering Control Systems at Nene College in Northampton. Prior to this he was a lecturer at the Open University where he contributed to distance learning courses on control engineering, electronics, mechatronics and artificial intelligence. His research interests include patten recognition, intelligent control and logic design.

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The origami introduced in this book is based on simple techniques. Some were previously known by origami artists and some were discovered by the author. Curved-Folding Origami Design shows a way to explore new area of origami composed of curved folds. Each technique is introduced in a step-by-step fashion, followed by some beautiful artwork examples. A commentary explaining the theory behind the technique is placed at the end of each chapter.Features Explains the techniques for designing curved-folding origami in seven chapters Contains many illustrations and photos (over 140 figures), with simple instructions Contains photos of 24 beautiful origami artworks, as well as their crease patterns Some basic theories behind the techniques are introduced

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Book SynopsisThis book is an essential supplement for MOST (Maynard Operation Sequence Technique) certification training. An excellent resource for practicing professionals and newcomers in the fields of industrial engineering and management, it provides a detailed explanation of each of the three MOST Systems. This edition is updated with relevant examples using todayâs technology to develop engineered standards. Content includes refreshed charts and guidelines to selecting a MOST System and completing a MOST analysis based on the application rules for BasicMOST, MiniMOST and MaxiMOST. A new informative chapter highlights the use of standards to improve workforce performance and increase productivity. A must for MOST certification for engineers, productivity improvement specialists, staffing, and costing professionals. Certification training can be completed online and worldwide through authorized partners. Table of Contents1. The Concept of MOST-An Introduction. 2. The MOST Systems Family. 3. The BasicMOST System. 4. The MiniMOST System. 5. The MaxiMOST System. 6. Computerized Work Measurement. 7. In Summary. Appendix A. Theory. Appendix B. Writing Method Step Descriptions. Appendix C. MOST Analysis Examples.

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Book SynopsisReliability and maintenance modeling with optimization is the most fundamental and interdisciplinary research area that can be applied to every technical and management field. Reliability and Maintenance Modeling with Optimization: Advances and Applications aims at providing the most recent advances and achievements in reliability and maintenance.The book discusses replacement, repair, and inspection, offers estimation and statistical tests, covers accelerated life testing, explores warranty analysis manufacturing, and includes service reliability.The targeted readers are researchers interested in reliability and maintenance engineering. The book can serve as supplemental reading in professional seminars for engineers, designers, project managers, and graduate students.Table of Contents1. Nine Memorial Research Works. 2. Replacement First and Last Policies with Random Times for Redundant Systems. 3. Backup Policies with Random Data Updates. 4. An Optimal Age Replacement Policy for a Reparable System Consisting of Main and Auxiliary Subsystems. 5. Extended replacement policy in damage models. 6. Optimal Checking Policy for a Server System with Cyber Attack. 7. Reliability Analysis of Congestion Control Scheme with Code Error Correction Methods. 8. The Optimal Design of Consecutive-k Systems. 9. Optimal Social Infrastructure Maintenance Models. 10. Optimal Maintenance Problem with OSS-Oriented EVM for OSS Project. 11. Reliability Assessment Model Based on Wiener Process Considering Network Environment for Edge Computing. 12. Approximated Estimation of Software Target Failure Measures Conforming IEC 61508. 13. Phase-Type Expansion of Markov Regenerative Processes and Its Application to Reliability Problems. 14. A Hybrid Model Fitting Framework considering Accuracy and Performance. 15. Alternating α-Series Process. 16. Optimum Staggered Testing Strategy for Redundant Safety Instrumented Systems with Different Testing Intervals. 17. Modules Of Multi-State Systems - Introduction To Three Modules Theorem. 18. A postponed repair model for a mission-based system based on a three-stage failure process.

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Book SynopsisReliability and maintenance modeling with optimization is the most fundamental and interdisciplinary research area that can be applied to every technical and management field. Reliability and Maintenance Modeling with Optimization: Advances and Applications aims at providing the most recent advances and achievements in reliability and maintenance.The book discusses replacement, repair, and inspection, offers estimation and statistical tests, covers accelerated life testing, explores warranty analysis manufacturing, and includes service reliability.The targeted readers are researchers interested in reliability and maintenance engineering. The book can serve as supplemental reading in professional seminars for engineers, designers, project managers, and graduate students.

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Book SynopsisThis book provides an introduction to MIP modeling and to planning systems, a unique collection of reformulation results, and an easy to use problem-solving library. This approach is demonstrated through a series of real life case studies, exercises and detailed illustrations.Trade ReviewFrom the reviews: "The book provides a complete overview of different models existing in the literature as well as in practice. … The authors also analyze MIP (mixed integer programming) based algorithms … . Practitioners who are interested in using MIP … can use the book to identify the most efficient way to formulate the problems and to choose the most efficient solution method. … it also can serve as a good reference for students and researchers. Overall, this is an excellent book." (Panos M. Pardalos, Mathematical Reviews, Issue 2006 k) "Recently published Production Planning by Mixed Integer Programming by Yves Pochet and Laurence Wolsey has raised considerable expectations. Firstly, problems of production planning are among the most interesting in Operations Research. … Secondly, both authors are renowned experts in the field. … There is no doubt that this volume offers the present best introduction to integer programming formulations of lot-sizing problems, encountered in production planning." (Jakub Marecek, The Computer Journal, September, 2007)Table of ContentsProduction Planning and MIP.- The Modeling and Optimization Approach.- Production Planning Models and Systems.- Mixed Integer Programming Algorithms.- Classification and Reformulation.- Reformulations in Practice.- Basic Polyhedral Combinatorics for Production Planning and MIP.- Mixed Integer Programming Algorithms and Decomposition Approaches.- Single-Item Uncapacitated Lot-Sizing.- Basic MIP and Fixed Cost Flow Models.- Single-Item Lot-Sizing.- Lot-Sizing with Capacities.- Backlogging and Start-Ups.- Single-Item Variants.- Multi-Item Lot-Sizing.- Multi-Item Single-Level Problems.- Multi-Level Lot-Sizing Problems.- Problem Solving.- Test Problems.

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Book SynopsisA comprehensive introduction to a wide variety of statistical methods for the analysis of repeated measurements. It is designed to be both a useful reference for practitioners and a textbook for a graduate-level course focused on methods for the analysis of repeated measurements.Trade ReviewFrom the reviews: MATHEMATICAL REVIEWS "…the book covers a wide range of topics, including inference based on normal theory, repeated categorical outcomes and missing values. The book is based on lecture notes used by the author since 1991. Hence, the material and the structure of the book have been well tested by different audiences. Another feature of the book is the inclusion of a very rich collection of problems with excellent real data. Thus, it is a nice textbook for a semester course on repeated measurements and longitudinal data." SHORT BOOK REVIEW "Each major topic is introduced logically; background theory is clearly elucidated, and at least one example is carefully worked in detail. The use of eighty real sets of data, given in full, is a most attractive feature. Attention is concentrated on those techniques that are most readily available in software. ... This should prove to be a very useful text for teacher, student and practitioner alike." JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION "Most other books on repeated measurements tend to focus on specialized topics. In my opinion, [this] book is the most comprehensive and readable of the lot. I would highly recommend its use as a text for a semester-length graduate course for biostatistics and statistics students and also as resource book for consulting biostatisticians and statisticians. In addition, this book would be a valuable resource for students from other fields of study (e.g., the health sciences) who have a statistical aptitude. The book is definitely worth the price." "The intention of the book is ‘to provide a reasonably comprehensive overview of methods for the analysis of repeated measurements’ with focus on standard statistical methods … . In my opinion the book gives a nice, comprehensive overview of methods for the analysis of repeated measurements. … The availability of data sets, overheads, etc. is a very valuable supplement for both teachers and students. … The book … could be a natural choice for a course in repeated measurements for graduate students in (bio-) statistics." (Niels Trolle Andersen, Statistics in Medicine, Vol. 24 (5), 2005) "This book is a very interesting and comprehensive summary of a wide selection of statistical methods for the analysis of repeated measurements. It is indeed an ideal and carefully written text to be used as a reference guide for practitioners and, in addition, as a great, up to date and very complete textbook for a graduate-level course in Statistics and/or Biostatistics. … I highly recommend Statistical Methods … as a good reference book for anyone interested in looking into the different available methodologies … ." (Vicente Núñez-Antón, Journal of Applied Statistics, Vol. 30 (10), December, 2003) "This book provides a comprehensive introduction to a wide variety of statistical methods for the analysis of repeated measurements. … In conclusion, as a course text on repeated measurements this book clearly has major strengths over others in that it provides coverage on a wide range of topics and provides extensive further reading material. … I would recommend this text as a general reference book on repeated measurements which would make a worthwhile addition to a departmental library." (Fiona Holland, Pharmaceutical Statistics, 2003) "Most other books on repeated measurements … tend to focus on specialized topics. In my opinion, Statistical Methods for the Analysis of Repeated Measurements book is the most comprehensive and readable of the lot. I would highly recommend its use as a text for a semester-length graduate course for biostatistics and statistics students and … for consulting biostatisticians and statisticians. … a valuable resource for students from other fields of study … who have a statistical aptitude. The book is definitely worth the price." (Melvin L. Moeschberger, Journal of the American Statistical Association, March, 2003) "The book aims at describing, discussing and demonstrating a variety of statistical methods for the analysis of repeated measurements … . the book covers a very wide range of topics, including inference based on normal theory, repeated categorical outcomes and missing values. … Another feature of the book is the inclusion of a very rich collection of problems with excellent real data. Thus, it is a nice textbook for a semester course on repeated measurements and longitudinal data." (Jack C. Lee, Mathematical Reviews, 2003 e) "This book is intended to provide a comprehensive introduction to a wide range of statistical methods for the analysis of repeated measurements. … For use in a course, I would use it for an applied graduate-level statistics course on linear models for analysis of repeated measurements. This text is useful not only with regards to the statistical methods, but also for the real data examples that can be explored with the various models and methods under study." (James R. Kenyon, Technometrics, Vol. 45 (1), 2003) "This book provides a reasonably comprehensive overview of a wide variety of statistical methods for the analysis of repeated measurements … . The important features of this book include a summary of both classical and recent methods for continuous and categorical outcome variables, numerous homework problems, and the extensive use of real data sets in examples. … This book will be of interest to graduate students in statistics and biostatistics as well as to practicing statisticians in academic, industry and research institutions.” (Ivan Krivý, Zentralblatt MATH, Vol. 985, 2002)Table of ContentsIntroduction * Univariate Methods * Normal-Theory Methods: Unstructured Multivariate Approach * Normal Theory Methods: Multivariate Analysis of Variance * Normal-Theory Methods: Repeated Measures ANOVA * Normal Theory Methods: Linear Mixed Models * Weighted Least Squares Analysis of Repeated Categorical Outcomes * Randomization Model Methods for One-Sample Repeated Measurements * Methods Based on Extensions of Generalized Linear Models * Nonparametric Methods

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Book SynopsisPorous media are broadly found in nature and their study is of high relevance in our present lives. In geosciences porous media research is fundamental in applications to aquifers, mineral mines, contaminant transport, soil remediation, waste storage, oil recovery and geothermal energy deposits. Despite their importance, there is as yet no complete understanding of the physical processes involved in fluid flow and transport. This fact can be attributed to the complexity of the phenomena which include multicomponent fluids, multiphasic flow and rock-fluid interactions. Since its formulation in 1856, Darcyâs law has been generalized to describe multi-phase compressible fluid flow through anisotropic and heterogeneous porous and fractured rocks. Due to the scarcity of information, a high degree of uncertainty on the porous medium properties is commonly present. Contributions to the knowledge of modeling flow and transport, as well as to the characterization of porous media at field scale are of great relevance. This book addresses several of these issues, treated with a variety of methodologies grouped into four parts:I Fundamental conceptsII Flow and transportIII Statistical and stochastic characterizationIV WavesThe problems analyzed in this book cover diverse length scales that range from small rock samples to field-size porous formations. They belong to the most active areas of research in porous media with applications in geosciences developed by diverse authors.This book was written for a broad audience with a prior and basic knowledge of porous media. The book is addressed to a wide readership, and it will be useful not only as an authoritative textbook for undergraduate and graduate students but also as a reference source for professionals including geoscientists, hydrogeologists, geophysicists, engineers, applied mathematicians and others working on porous media.Table of ContentsSection 1: Fundamental concepts Section 2: Flow and transport Section 3: Statistical and stochastic characterization Section 4: Waves

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Book SynopsisA balanced introduction to the theoretical foundations and real-world applications of mathematical finance The ever-growing use of derivative products makes it essential for financial industry practitioners to have a solid understanding of derivative pricing. To cope with the growing complexity, narrowing margins, and shortening life-cycle of the individual derivative product, an efficient, yet modular, implementation of the pricing algorithms is necessary. Mathematical Finance is the first book to harmonize the theory, modeling, and implementation of today''s most prevalent pricing models under one convenient cover. Building a bridge from academia to practice, this self-contained text applies theoretical concepts to real-world examples and introduces state-of-the-art, object-oriented programming techniques that equip the reader with the conceptual and illustrative tools needed to understand and develop successful derivative pricing models. Utilizing almost tweTrade Review"…very useful to practitioners and students…" (MAA Reviews, December 26, 2007) "An excellent textbook for students in mathematical finance, computational finance, and derivative pricing courses at the upper undergraduate or beginning graduate level." (Mathematical Reviews 2007)Table of Contents1. Introduction. 1.1 Theory, Modeling and Implementation. 1.2 Interest Rate Models and Interest Rate Derivatives. 1.3 How to Read this Book. 1.3.1 Abridged Versions. 1.3.2 Special Sections. 1.3.3 Notation. I: FOUNDATIONS. 2. Foundations. 2.1 Probability Theory. 2.2 Stochastic Processes. 2.3 Filtration. 2.4 Brownian Motion. 2.5 Wiener Measure, Canonical Setup. 2.6 Itô Calculus. 2.6.1 Itô Integral. 2.6.2 Itô Process. 2.6.3 Itô Lemma and Product Rule. 2.7 Brownian Motion with Instantaneous Correlation. 2.8 Martingales. 2.8.1 Martingale Representation Theorem. 2.9 Change of Measure (Girsanov, Cameron, Martin). 2.10 Stochastic Integration. 2.11 Partial Differential Equations (PDE). 2.11.1 Feynman-Kac Theorem . 2.12 List of Symbols. 3. Replication. 3.1 Replication Strategies. 3.1.1 Introduction. 3.1.2 Replication in a discrete Model. 3.2 Foundations: Equivalent Martingale Measure. 3.2.1 Challenge and Solution Outline. 3.2.2 Steps towards the Universal Pricing Theorem. 3.3 Excursus: Relative Prices and Risk Neutral Measures. 3.3.1 Why relative prices? 3.3.2 Risk Neutral Measure. II: FIRST APPLICATIONS. 4. Pricing of a European Stock Option under the Black-Scholes Model. 5. Excursus: The Density of the Underlying of a European Call Option. 6. Excursus: Interpolation of European Option Prices. 6.1 No-Arbitrage Conditions for Interpolated Prices. 6.2 Arbitrage Violations through Interpolation. 6.2.1 Example (1): Interpolation of four Prices. 6.2.2 Example (2): Interpolation of two Prices. 6.3 Arbitrage-Free Interpolation of European Option Prices. 7. Hedging in Continuous and Discrete Time and the Greeks. 7.1 Introduction. 7.2 Deriving the Replications Strategy from Pricing Theory. 7.2.1 Deriving the Replication Strategy under the Assumption of a Locally Riskless Product. 7.2.2 The Black-Scholes Differential Equation. 7.2.3 The Derivative V(t) as a Function of its Underlyings S i(t). 7.2.4 Example: Replication Portfolio and PDE under a Black-Scholes Model. 7.3 Greeks. 7.3.1 Greeks of a European Call-Option under the Black-Scholes model. 7.4 Hedging in Discrete Time: Delta and Delta-Gamma Hedging. 7.4.1 Delta Hedging. 7.4.2 Error Propagation. 7.4.3 Delta-Gamma Hedging. 7.4.4 Vega Hedging. 7.5 Hedging in Discrete Time: Minimizing the Residual Error (Bouchaud-Sornette Method). 7.5.1 Minimizing the Residual Error at Maturity T. 7.5.2 Minimizing the Residual Error in each Time Step. III: INTEREST RATE STRUCTURES, INTEREST RATE PRODUCTS AND ANALYTIC PRICING FORMULAS. Motivation and Overview. 8. Interest Rate Structures. 8.1 Introduction. 8.1.1 Fixing Times and Tenor Times. 8.2 Definitions. 8.3 Interest Rate Curve Bootstrapping. 8.4 Interpolation of Interest Rate Curves. 8.5 Implementation. 9. Simple Interest Rate Products. 9.1 Interest Rate Products Part 1: Products without Optionality. 9.1.1 Fix, Floating and Swap. 9.1.2 Money-Market Account. 9.2 Interest Rate Products Part 2: Simple Options. 9.2.1 Cap, Floor, Swaption. 9.2.2 Foreign Caplet, Quanto. 10. The Black Model for a Caplet. 11. Pricing of a Quanto Caplet (Modeling the FFX). 11.1 Choice of Numéraire. 12. Exotic Derivatives. 12.1 Prototypical Product Properties. 12.2 Interest Rate Products Part 3: Exotic Interest Rate Derivatives. 12.2.1 Structured Bond, Structured Swap, Zero Structure. 12.2.2 Bermudan Option. 12.2.3 Bermudan Callable and Bermudan Cancelable. 12.2.4 Compound Options. 12.2.5 Trigger Products. 12.2.6 Structured Coupons. 12.2.7 Shout Options. 12.3 Product Toolbox. IV: DISCRETIZATION AND NUMERICAL VALUATION METHODS. Motivation and Overview. 13. Discretization of time and state space. 13.1 Discretization of Time: The Euler and the Milstein Scheme. 13.1.1 Definitions. 13.1.2 Time-Discretization of a Lognormal Process. 13.2 Discretization of Paths (Monte-Carlo Simulation) . 13.2.1 Monte-Carlo Simulation. 13.2.2 Weighted Monte-Carlo Simulation. 13.2.3 Implementation. 13.2.4 Review. 13.3 Discretization of State Space. 13.3.1 Definitions. 13.3.2 Backward-Algorithm. 13.3.3 Review. 13.4 Path Simulation through a Lattice: Two Layers. 14. Numerical Methods for Partial Differential Equations. 15. Pricing Bermudan Options in a Monte Carlo Simulation. 15.1 Introduction. 15.2 Bermudan Options: Notation. 15.2.1 Bermudan Callable. 15.2.2 Relative Prices. 15.3 Bermudan Option as Optimal Exercise Problem. 15.3.1 Bermudan Option Value as single (unconditioned) Expectation: The Optimal Exercise Value. 15.4 Bermudan Option Pricing - The Backward Algorithm. 15.5 Re-simulation. 15.6 Perfect Foresight. 15.7 Conditional Expectation as Functional Dependence. 15.8 Binning. 15.8.1 Binning as a Least-Square Regression. 15.9 Foresight Bias. 15.10 Regression Methods - Least Square Monte-Carlo. 15.10.1 Least Square Approximation of the Conditional Expectation. 15.10.2 Example: Evaluation of a Bermudan Option on a Stock (Backward Algorithm with Conditional Expectation Estimator). 15.10.3 Example: Evaluation of a Bermudan Callable. 15.10.4 Implementation. 15.10.5 Binning as linear Least-Square Regression. 15.11 Optimization Methods. 15.11.1 Andersen Algorithm for Bermudan Swaptions. 15.11.2 Review of the Threshold Optimization Method. 15.11.3 Optimization of Exercise Strategy: A more general Formulation. 15.11.4 Comparison of Optimization Method and Regression. Method. 15.12 Duality Method: Upper Bound for Bermudan Option Prices. 15.12.1 Foundations. 15.12.2 American Option Evaluation as Optimal Stopping Problem. 15.13 Primal-Dual Method: Upper and Lower Bound. 16. Pricing Path-Dependent Options in a Backward Algorithm. 16.1 Evaluation of a Snowball / Memory in a Backward Algorithm. 16.2 Evaluation of a Flexi Cap in a Backward Algorithm. 17. Sensitivities (Partial Derivatives) of Monte Carlo Prices. 17.1 Introduction. 17.2 Problem Description. 17.2.1 Pricing using Monte-Carlo Simulation. 17.2.2 Sensitivities from Monte-Carlo Pricing. 17.2.3 Example: The Linear and the Discontinuous Payout. 17.2.4 Example: Trigger Products. 17.3 Generic Sensitivities: Bumping the Model. 17.4 Sensitivities by Finite Differences. 17.4.1 Example: Finite Differences applied to Smooth and Discontinuous Payout. 17.5 Sensitivities by Pathwise Differentiation. 17.5.1 Example: Delta of a European Option under a Black-Scholes Model. 17.5.2 Pathwise Differentiation for Discontinuous Payouts. 17.6 Sensitivities by Likelihood Ratio Weighting. 17.6.1 Example: Delta of a European Option under a Black-Scholes Model using Pathwise Derivative. 17.6.2 Example: Variance Increase of the Sensitivity when using Likelihood Ratio Method for Smooth Payouts. 17.7 Sensitivities by Malliavin Weighting. 17.8 Proxy Simulation Scheme. 18. Proxy Simulation Schemes for Monte Carlo Sensitivities and Importance Sampling. 18.1 Full Proxy Simulation Scheme. 18.1.1 Calculation of Monte-Carlo weights. 18.2 Sensitivities by Finite Differences on a Proxy Simulation Scheme. 18.2.1 Localization. 18.2.2 Object-Oriented Design. 18.3 Importance Sampling. 18.3.1 Example. 18.4 Partial Proxy Simulation Schemes. 18.4.1 Linear Proxy Constraint. 18.4.2 Comparison to Full Proxy Scheme Method. 18.4.3 Non-Linear Proxy Constraint. 18.4.4 Transition Probability from a Nonlinear Proxy Constraint. 18.4.5 Sensitivity with respect to the Diffusion Coefficients - Vega. 18.4.6 Example: LIBOR Target Redemption Note. 18.4.7 Example: CMS Target Redemption Note. V: PRICING MODELS FOR INTEREST RATE DERIVATIVES. 19. LIBOR Market Models. 19.1 LIBOR Market Model. 19.1.1 Derivation of the Drift Term. 19.1.2 The Short Period Bond P(Tm(t)+1;t) . 19.1.3 Discretization and (Monte-Carlo) Simulation. 19.1.4 Calibration - Choice of the free Parameters. 19.1.5 Interpolation of Forward Rates in the LIBOR Market Model. 19.2 Object Oriented Design. 19.2.1 Reuse of Implementation. 19.2.2 Separation of Product and Model. 19.2.3 Abstraction of Model Parameters. 19.2.4 Abstraction of Calibration. 19.3 Swap Rate Market Models (Jamshidian 1997). 19.3.1 The Swap Measure. 19.3.2 Derivation of the Drift Term. 19.3.3 Calibration - Choice of the free Parameters. 20. Swap Rate Market Models. 20.1 Definitions. 20.2 Terminal Correlation examined in a LIBOR Market Model Example. 20.2.1 De-correlation in a One-Factor Model. 20.2.2 Impact of the Time Structure of the Instantaneous Volatility on Caplet and Swaption Prices. 20.2.3 The Swaption Value as a Function of Forward Rates. 20.3 Terminal Correlation is dependent on the Equivalent Martingale Measure. 20.3.1 Dependence of the Terminal Density on the Martingale Measure. 21. Excursus: Instantaneous Correlation and Terminal Correlation. 21.1 Short Rate Process in the HJM Framework. 21.2 The HJM Drift Condition. 22.Heath-Jarrow-Morton Framework: Foundations. 22.1 Introduction. 22.2 The Market Price of Risk. 22.3 Overview: Some Common Models. 22.4 Implementations. 22.4.1 Monte-Carlo Implementation of Short-Rate Models. 22.4.2 Lattice Implementation of Short-Rate Models. 23. Short-Rate Models. 23.1 Short Rate Models in the HJM Framework. 23.1.1 Example: The Ho-Lee Model in the HJM Framework. 23.1.2 Example: The Hull-White Model in the HJM Framework. 23.2 LIBOR Market Model in the HJM Framework. 23.2.1 HJM Volatility Structure of the LIBOR Market Model. 23.2.2 LIBOR Market Model Drift under the QB Measure. 23.2.3 LIBOR Market Model as a Short Rate Model. 24 Heath-Jarrow-Morton Framwork: Immersion of Short-Rate Models and LIBOR Market Model. 24.1 Model. 24.2 Interpretation of the Figures. 24.3 Mean Reversion. 24.4 Factors. 24.5 Exponential Volatility Function. 24.6 Instantaneous Correlation. 25. Excursus: Shape of teh Interst Rate Curve under Mean Reversion and a Multifactor Model. 25.1 Introduction. 25.2 Cheyette Model. 26. Ritchken-Sakarasubramanian Framework: JHM with Low Markov Dimension. 26.1 Introduction. 26.1.1 The Markov Functional Assumption (independent of the model considered) . 26.1.2 Outline of this Chapter . 26.2 Equity Markov Functional Model. 26.2.1 Markov Functional Assumption. 26.2.2 Example: The Black-Scholes Model. 26.2.3 Numerical Calibration to a Full Two-Dimensional European Option Smile Surface. 26.2.4 Interest Rates. 26.2.5 Model Dynamics. 26.2.6 Implementation. 26.3 LIBOR Markov Functional Model. 26.3.1 LIBOR Markov Functional Model in Terminal Measure. 26.3.2 LIBOR Markov Functional Model in Spot Measure. 26.3.3 Remark on Implementation. 26.3.4 Change of numéraire in a Markov-Functional Model. 26.4 Implementation: Lattice. 26.4.1 Convolution with the Normal Probability Density. 26.4.2 State space discretization. Markov Functional Models. PART VI: Extended Models. 27.1 Introduction - Different Types of Spreads. 27.1.1 Spread on a Coupon. 27.1.2 Credit Spread. 27.2 Defaultable Bonds. 27.3 Integrating deterministic Credit Spread into a Pricing Model. 27.3.1 Deterministic Credit Spread. 27.3.2 Implementation. 27.4 Receiver’s and Payer’s Credit Spreads. 27.4.1 Example: Defaultable Forward Starting Coupon Bond. 27.4.2 Example: Option on a Defaultable Coupon Bond. 28. Credit Spreads. 28.1 Cross Currency LIBOR Market Model. 28.1.1 Derivation of the Drift Term under Spot-Measure. 28.1.2 Implementation. 28.2 Equity Hybrid LIBOR Market Model. 28.2.1 Derivation of the Drift Term under Spot-Measure. 28.2.2 Implementation. 28.3 Equity-Hybrid Cross-Currency LIBOR Market Model. 28.3.1 Summary. 28.3.2 Implementation. 29. Hybrid Models. 29.1 Elements of Object Oriented Programming: Class and Objects. 29.1.1 Example: Class of a Binomial Distributed Random Variable. 29.1.2 Constructor. 29.1.3 Methods: Getter, Setter, Static Methods. 29.2 Principles of Object Oriented Programming. 29.2.1 Encapsulation and Interfaces. 29.2.2 Abstraction and Inheritance. 29.2.3 Polymorphism. 29.3 Example: A Class Structure for One Dimensional Root Finders. 29.3.1 Root Finder for General Functions. 29.3.2 Root Finder for Functions with Analytic Derivative: Newton Method. 29.3.3 Root Finder for Functions with Derivative Estimation: Secant Method. 29.4 Anatomy of a Java™ Class. 29.5 Libraries. 29.5.1 Java™2 Platform, Standard Edition (j2se). 29.5.2 Java™2 Platform, Enterprise Edition (j2ee). 29.5.3 Colt. 29.5.4 Commons-Math: The Jakarta Mathematics Library. 29.6 Some Final Remarks. 29.6.1 Object Oriented Design (OOD) / Unified Modeling Language. PART VII: Implementation 30. Object-Oriented Implementatin in JavaTM. PART VIII: Appendices. A: A small Collection of Common Misconceptions. B: Tools (Selection). B.1 Linear Regression. B.2 Generation of Random Numbers. B.2.1 Uniform Distributed Random Variables. B.2.2 Transformation of the Random Number Distribution via the Inverse Distribution Function. B.2.3 Normal Distributed Random Variables. B.2.4 Poisson Distributed Random Variables. B.2.5 Generation of Paths of an n-dimensional Brownian Motion. B.3 Factor Decomposition - Generation of Correlated Brownian Motion. B.4 Factor Reduction. B.5 Optimization (one-dimensional): Golden Section Search. B.6 Convolution with Normal Density. C: Exercises. D: List of Symbols. E: Java™ Source Code (Selection). E.1 Java™ Classes for Chapter 29. List of Figures. List of Tables. List of Listings. Bibliography. Index.

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Book SynopsisAn accessible and multidisciplinaryintroduction to cellular automata As the applicability of cellular automata broadens and technology advances, there is a need for a concise, yet thorough, resource that lays the foundation of key cellularautomata rules and applications. In recent years, Stephen Wolfram''s A New Kind of Science has brought the modeling power that lies in cellular automata to the attentionof the scientific world, and now, Cellular Automata: A Discrete View of the World presents all the depth, analysis, and applicability of the classic Wolfram text in a straightforward, introductory manner. This book offers an introduction to cellular automata as a constructive method for modeling complex systems where patterns of self-organization arising from simple rules are revealed in phenomena that exist across a wide array of subject areas, including mathematics, physics, economics, and the social sciences. The book begins with a preliminary introduction to cellular autTrade Review"The book is well produced and a good introduction to its subject." (Computing Reviews, January 30, 2009) "An interesting read and worth browsing by somebody interested in getting a general background on CA. The examples are many and varied, and the numerous citations--both to electronic and printed media--are very helpful." (Computing Reviews, November 11, 2008) "An interesting read and worth browsing by somebody interested in getting a general background on CA. The examples are many and varied, and the numerous citations--both to electronic and printed media--are very helpful." (Computing Reviews, Nov 2008) "Schiff suppresses most mathematical details, rendering his book highly accessible, informative, and entertaining, but leaving open niches for a textbook treatment with exercises or an advanced monograph with proofs." (CHOICE, October 2008) "This book serves as a valuable resource for undergraduate and graduate students in the physical, biological, and social sciences and may also be of interest to any reader with a scientific or basic mathematical ground." (Mathematical Reviews, 2008m) "Schiff suppresses most mathematical details, rendering his book highly accessible, informative, and entertaining, but leaving open niches for a textbook treatment with exercises or an advanced monograph with proofs." (CHOICE Oct 2008) "This book serves as a valuable resource for undergraduate and graduate students in the physical, biological, and social sciences and may also be of interest to any reader with a scientific or basic mathematical ground." (Mathematical Reviews 2008)Table of ContentsPreface. 1. Preliminaries. 2. Dynamical Systems. 3. One-Dimensional Cellular Automata. 4. Two-Dimensional Automata. 5. Applications. 6. Complexity. Appendix A. References. Index.

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Book SynopsisPraise for the First Edition This...novel and highly stimulating book, which emphasizes solving real problems...should be widely read. It will have a positive and lasting effect on the teaching of modeling and statistics in general. - Short Book Reviews This new edition features developments and real-world examples that showcase essential empirical modeling techniques Successful empirical model building is founded on the relationship between data and approximate representations of the real systems that generated that data. As a result, it is essential for researchers who construct these models to possess the special skills and techniques for producing results that are insightful, reliable, and useful. Empirical Model Building: Data, Models, and Reality, Second Edition presents a hands-on approach to the basic principles of empirical model building through a shrewd mixture of differential equations, computer-intensive methods, and data. The Trade Review“It is also an excellent reference for applied statisticians and researchers who carry out quantitative modeling in their everyday work.” (Mathematical Reviews, 2012) "Empirical Model Building, Second Edition is an excellent reference for applied statisticians and researchers who carry out quantitative modeling in their everyday work." (TMCnet.com, 18 January 2012) Table of ContentsPreface. 1.Models of Growth and Decay. 2. Models of Competition, Survival, and Combat. 3. Epidemics. 4. Bootstrapping. 5. Monte-Carlo Solution of Differential Equations. 6. SIMEST, SMIDAT, and Psuedoreality. 7. Exploratory Data Analysis. 8. Noise Killing Chaos. 9. Bayesian Approaches. 10. Multivariate and Robust Procedures in Statistical Process Control. 11. Optimization and Estimation in the Real (Noisy) World. 12. Utility and Group Preference. 13. A Primer in Sampling. 14. Stock Market: Strategies Based on Data versus Strategies Based on Ideology. Appendix A. A Brief Introduction to Probability and Statistics. Appendix B. Statistical Tables.

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Book Synopsisone-of-a-kind introduction to the theory and application of modeling and simulation techniques in the realm of international studies Modeling and Simulation for Analyzing Global Events provides an orientation to the theory and application of modeling and simulation techniques in social science disciplines.Table of ContentsPreface. I PRINCIPLES OF MODELING AND SIMULATION: ADVANCING GLOBAL STUDIES. 1 Modeling and Simulation: What, When, and Why. Introduction. An Overview of Modeling and Simulation. A Brief History of Modeling and Simulation. Why Use Modeling and Simulation. Conclusions. Key Terms. References. Further Reading. 2 Research Methodologies for Modeling Global Events. Introduction. Global Events and the Social Sciences. Qualitative and Quantitative Research. Modeling and Simulation of Global Events. Mapping Data: A Suggested Methodology. Model Validation. Conclusions. Key Terms. References. II MODELING PARADIGMS. 3 System Dynamics. Introduction. Dynamic System Behavior. Building Blocks of System Dynamics Models. Conclusions. Key Terms. References. 4 Agent-Based Modeling and Social Networks. Introduction. Agent-Based Models: Description and Definition. Social Networks. Building an Agent-Based Model. Conclusions. Key Terms. References. 5 Game Theory. Introduction. Fundamentals of Game Theory. Types of Games. Conclusions. Key Terms. References. III MODELING GLOBAL EVENTS. 6 Case Study: Colombia—A Country Study on Insurgency. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Mapping Qualitative to Quantitative. System Dynamics. Responding to the Research Question. Key Terms. References. Case Study Bibliography. 7 Case Study: The Polish Solidarity Movement—Laying the Foundation for the Collapse of Soviet Communism. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Measuring Agents and Environments: Stimuli and Actions. Modeling Human Behavior with Agents. Responding to the Research Question. Conclusions. Key Terms. References. Case Study Bibliography. 8 Case Study: Vietnam—Johnson’s War, 1963–1965. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Analyzing the Social Network Structures. Social Network Aspects of Human Behavior Modeling. Agent-Based Model Development. Responding to the Research Question. Key Terms. References. Case Study Bibliography. 9 Case Study: Cuban Missile Crisis—A National Security Emergency. Introduction. Developing the Research Question and Methodology. Background: Qualitative Research. Evaluating Behaviors. Game Theory. Responding to the Research Question. Key Terms. References. Case Study Bibliography. Index.

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Book SynopsisAn accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction andmotivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysisand variance reduction; and applications ranging from option pricing and risk management to optimization. The Handbook in Monte Carlo Simulation features: An introductorTable of ContentsPreface xiii Part I Overview and Motivation 1 Introduction to Monte Carlo Methods 3 1.1 Historical origin of Monte Carlo simulation 4 1.2 Monte Carlo Simulation vs. Monte Carlo Sampling 7 1.3 System dynamics and the mechanics of Monte Carlo simulation 10 1.4 Simulation and optimization 21 1.5 Pitfalls in Monte Carlo simulation 30 1.6 Software tools for Monte Carlo simulation 35 1.7 Prerequisites 37 For further reading 38 Chapter References 38 2 Numerical Integration Methods 41 2.1 Classical quadrature formulae 43 2.2 Gaussian quadrature 48 2.3 Extension to higher dimensions: Product rules 53 2.4 Alternative approaches for high-dimensional integration 55 2.5 Relationship with moment matching 67 2.6 Numerical integration in R 69 For further reading 71 Chapter References 71 Part II Input Analysis: Modeling and Estimation 3 Stochastic Modeling in Finance and Economics 75 3.1 Introductory examples 77 3.2 Some common probability distributions 86 3.3 Multivariate distributions: Covariance and correlation 111 3.4 Modeling dependence with copulae 127 3.5 Linear regression models: a probabilistic view 136 3.6 Time series models 137 3.7 Stochastic differential equations 158 3.8 Dimensionality reduction 177 S3.1 Risk-neutral derivative pricing 190 S3.1.1 Option pricing in the binomial model 192 S3.1.2 A continuous-time model for option pricing: The Black–Scholes–Merton formula 194 S3.1.3 Option pricing in incomplete markets 199 For further reading 202 Chapter References 203 4 Estimation and Fitting 205 4.1 Basic inferential statistics in R 207 4.2 Parameter estimation 215 4.3 Checking the fit of hypothetical distributions 224 4.4 Estimation of linear regression models by ordinary least squares 229 4.5 Fitting time series models 232 4.6 Subjective probability: the Bayesian view 235 For further reading 244 Chapter References 245 Part III Sampling and Path Generation 5 Random Variate Generation 249 5.1 The structure of a Monte Carlo simulation 250 5.2 Generating pseudo-random numbers 252 5.3 The inverse transform method 263 5.4 The acceptance–rejection method 265 5.5 Generating normal variates 269 5.6 Other ad hoc methods 274 5.7 Sampling from copulae 276 For further reading 277 Chapter References 279 6 Sample Path Generation for Continuous-Time Models 281 6.1 Issues in path generation 282 6.2 Simulating geometric Brownian motion 287 6.3 Sample paths of short-term interest rates 298 6.4 Dealing with stochastic volatility 306 6.5 Dealing with jumps 308 For further reading 310 Chapter References 311 Part IV Output Analysis and Efficiency Improvement 7 Output Analysis 315 7.1 Pitfalls in output analysis 317 7.2 Setting the number of replications 323 7.3 A world beyond averages 325 7.4 Good and bad news 327 For further reading 327 Chapter References 328 8 Variance Reduction Methods 329 8.1 Antithetic sampling 330 8.2 Common random numbers 336 8.3 Control variates 337 8.4 Conditional Monte Carlo 341 8.5 Stratified sampling 344 8.6 Importance sampling 350 For further reading 363 Chapter References 363 9 Low-Discrepancy Sequences 365 9.1 Low-discrepancy sequences 366 9.2 Halton sequences 367 9.3 Sobol low-discrepancy sequences 374 9.4 Randomized and scrambled low-discrepancy sequences 379 9.5 Sample path generation with low-discrepancy sequences 381 For further reading 385 Chapter References 385 Part V Miscellaneous Applications 10 Optimization 389 10.1 Classification of optimization problems 390 10.2 Optimization model building 405 10.3 Monte Carlo methods for global optimization 412 10.4 Direct search and simulation-based optimization methods 416 10.5 Stochastic programming models 420 10.6 Scenario generation and Monte Carlo methods for stochastic programming 428 10.7 Stochastic dynamic programming 433 10.8 Numerical dynamic programming 440 10.9 Approximate dynamic programming 451 For further reading 453 Chapter References 453 11 Option Pricing 455 11.1 European-style multidimensional options in the BSM world 456 11.2 European-style path-dependent options in the BSM world 462 11.3 Pricing options with early exercise features 475 11.4 A look outside the BSM world 487 11.5 Pricing interest-rate derivatives 490 For further reading 497 Chapter References 498 12 Sensitivity Estimation 501 12.1 Estimating option greeks by finite differences 503 12.2 Estimating option greeks by pathwise derivatives 509 12.3 Estimating option greeks by the likelihood ratio method 513 For further reading 517 Chapter References 518 13 Risk Measurement and Management 519 13.1 What is a risk measure? 520 13.2 Quantile-based risk measures: value at risk 522 13.3 Monte Carlo methods for V@R 533 13.4 Mean-risk models in stochastic programming 537 13.5 Simulating delta-hedging strategies 540 13.6 The interplay of financial and nonfinancial risks 546 For further reading 548 Chapter References 548 14 Markov Chain Monte Carlo and Bayesian Statistics 551 14.1 An introduction to Markov chains 552 14.2 The Metropolis–Hastings algorithm 555 14.3 A re-examination of simulated annealing 558 For further reading 560 Chapter References 561 Index 563

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