Simulation and the Monte Carlo Method: Solutions Manual to Accompany, Second Edition By Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev, Rueven Y. Rubinstein(auth.)
2008 | 186 Pages | ISBN: 0470258799 | PDF | 8 MB
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® 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. Content: Chapter 1 Preliminaries (pages xi-7): Chapter 2 Random Number, Random Variable, and Stochastic Process Generation (pages 9-13): Chapter 3 Simulation of Discrete?Event Systems (pages 15-17): Chapter 4 Statistical Analysis of Discrete?Event Systems (pages 19-23): Chapter 5 Controlling the Variance (pages 25-30): Chapter 6 Markov Chain Monte Carlo (pages 31-36): Chapter 7 Sensitivity Analysis and Monte Carlo Optimization (pages 37-40): Chapter 8 The Cross?Entropy Method (pages 41-46): Chapter 9 Counting via Monte Carlo (pages 47-49): Chapter 10 Appendix (pages 51-52): Chapter 11 Preliminaries (pages 53-68): Chapter 12 Random Number, Random Variable, and Stochastic Process Generation (pages 69-83): Chapter 13 Simulation of Discrete?Event Systems (pages 85-94): Chapter 14 Statistical Analysis of Discrete?Event Systems (pages 95-103): Chapter 15 Controlling the Variance (pages 105-121): Chapter 16 Markov Chain Monte Carlo (pages 123-144): Chapter 17 Sensitivity Analysis and Monte Carlo Optimization (pages 145-150): Chapter 18 The Cross?Entropy Method (pages 151-175): Chapter 19 Counting via Monte Carlo (pages 177-186): Chapter 20 Appendix (pages 187-188):
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