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Wiley Series in Probability and Statistics Ser.: Student Solutions Manual to Accompany Simulation and the Monte Carlo Method by Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev and Reuven Y. Rubinstein (2007, Perfect)
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About this product
Product Identifiers
PublisherWiley & Sons, Incorporated, John
ISBN-100470258799
ISBN-139780470258798
eBay Product ID (ePID)63181788
Product Key Features
Number of Pages208 Pages
LanguageEnglish
Publication NameStudent Solutions Manual to Accompany Simulation and the Monte Carlo Method
SubjectProbability & Statistics / Stochastic Processes, Computer Simulation, Probability & Statistics / General
Publication Year2007
TypeTextbook
AuthorDirk P. Kroese, Thomas Taimre, Zdravko I. Botev, Reuven Y. Rubinstein
Subject AreaMathematics, Computers
SeriesWiley Series in Probability and Statistics Ser.
FormatPerfect
Dimensions
Item Height0.6 in
Item Weight9.6 Oz
Item Length9 in
Item Width5.9 in
Additional Product Features
Edition Number2
Intended AudienceScholarly & Professional
Series Volume Number732
IllustratedYes
Edition DescriptionStudent edition
Table Of ContentPreface. Acknolwedgments. I: Problems. 1. Preliminaries. 2. Random Number, random Variable, and Stochastic Process Generation. 3. Simulatin of Discrete-Event Systems. 4. Stastical Analysis of Discrete-Event Systems. 5. Controlling the Variance. 6. Markov Chain Monte Carlo. 7. Sensitivity Analysis and Monte Carlo Optimization. 8. The Cross-Entropy Method. 9. Counting via Monte Carlo. 10. Appendix. II: Solutions. 11. Prelimiaries. 12. Random Number, Random Variable, and Stochastic Process Generation. 13. Simulatin of Discrete-Event Systems. 14. Stastical Analysis of Discrete-Event Systems. 15. Controlling the Variance. 16. Markov Chain Monte Carlo. 17. Sensitivity Analysis and Monte Carlo Optimization. 18. The Cross-Entropy Method. 19. Counting via Monte Carlo. 20. Appendix.
SynopsisThis accessible new edition explores the major topics in Monte Carlo simulation Simulation and the Monte Carlo Method, Second Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over twenty-five years ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo Variance reduction techniques such as the transform likelihood ratio method and the screening method The score function method for sensitivity analysis The stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization The cross-entropy method to rare events estimation and combinatorial optimization Application of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy method An extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly for advanced readers. A generous sampling of applied examples is positioned throughout the book, emphasizing various areas of application, and a detailed appendix presents an introduction to exponential families, a discussion of the computational complexity of stochastic programming problems, and sample MATLAB® programs. Requiring only a basic, introductory knowledge of probability and statistics, Simulation and the Monte Carlo Method, Second Edition is an excellent text for upper-undergraduate and beginning graduate courses in simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method., This long awaited Second Edition gives a fully updated and comprehensive account of the major topics in Monte Carlo Method simulation since the early 1980s. The book is geared to a broad audience of readers in engineering, the physical and life sciences, statistics, computer science, and mathematics., This book represents one of the first modern day treatments of Monte Carlo Methods (MCM). Since the publication of the first edition, dramatic changes have taken place in the entire field. This long awaited second edition gives a fully updated and comprehensive account of the major topics in Monte Carlo simulation since the early 1980's. The book is geared to a broad audience of readers in engineering, the physical and life sciences, statistics, computer science, and mathematics. The authors aim to provide an accessible introduction to modern MCM, focusing on the main concepts, while providing a sound foundation for problem solving. For this reason, most ideas are introduced and explained by way of concrete examples, algorithms, and practical experiments., This accessible new edition explores the major topics in Monte Carlo simulation Simulation and the Monte Carlo Method, Second Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over twenty-five years ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo Variance reduction techniques such as the transform likelihood ratio method and the screening method The score function method for sensitivity analysis The stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization The cross-entropy method to rare events estimation and combinatorial optimization Application of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy method An extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly for advanced readers. A generous sampling of applied examples is positioned throughout the book, emphasizing various areas of application, and a detailed appendix presents an introduction to exponential families, a discussion of the computational complexity of stochastic programming problems, and sample MATLAB(R) programs. Requiring only a basic, introductory knowledge of probability and statistics, Simulation and the Monte Carlo Method, Second Edition is an excellent text for upper-undergraduate and beginning graduate courses in simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method.
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