Simulation and the Monte Carlo Method

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John Wiley & Sons, 20 de set. 2011 - 372 pàgines
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.

 

Pàgines seleccionades

Continguts

Preface
Random Number Random Variable
Problems
Simulation of DiscreteEvent Systems
Counting viaMonte Carlo
Markov ChainMonte Carlo
Statistical Analysisof DiscreteEvent Systems 4 1 Introduction 4 2 Static Simulation Models
Controlling theVariance
Problems
Sensitivity Analysisand MonteCarlo Optimization
The CrossEntropy Method
and Decision Making 9 7 Numerical Results
Appendix
Sensitivity Analysis
Abbreviations and Acronyms
Index

Sampling
Copyright

Altres edicions - Mostra-ho tot

Simulation and the Monte Carlo Method
Reuven Y. Rubinstein,Dirk P. Kroese
Previsualització no disponible - 2007
Simulation and the Monte Carlo Method
Reuven Y. Rubinstein
Previsualització no disponible - 1991

Frases i termes més freqüents

acceptance­rejection algorithm Analysis apply assume batch means Bayesian bottleneck parameters canbe combinatorial optimization components conditional confidence interval Consider converges convex corresponding covariance cross­entropy defined denote density discrete event example exponential family exponentially distributed Figure Gibbs sampler given graph hit­and­run importance sampling estimator independent inverse­transform iteration i­th likelihood ratio likelihood ratio estimator Markov chain matrix MCMC method Metropolis­Hastings minimal MinxEnt Monte Carlo n­dimensional normalization constant Note number of customers obtained ofthe optimal solution optimization problem parameter vector path pdf f(x performance permutation Poisson process queue random sample random variables random vector random walk rare­event probability reference parameter regenerative respectively Rubinstein simulated annealing so­called solve Statistical steady­state stochastic approximation stochastic counterpart stochastic programming Suppose Table thatthe theorem tothe transition typical uniformly distributed values variance reduction

Sobre l'autor (2011)

Reuven Y. Rubinstein, DSc, is Professor Emeritus in the Faculty of Industrial Engineering and Management at Technion-Israel Institute of Technology. He has served as a consultant at numerous large-scale organizations, such as IBM, Motorola, and NEC. The author of over 100 articles and six books, Dr. Rubinstein is also the inventor of the popular score-function method in simulation analysis and generic cross-entropy methods for combinatorial optimization and counting.

Dirk P. Kroese, PhD, is Senior Lecturer in Statistics in the Department of Mathematics at The University of Queensland, Australia. He has published over fifty articles in a wide range of areas in applied probability and statistics, including Monte Carlo methods, cross-entropy, randomized algorithms, tele-traffic theory, reliability, computational statistics, applied probability, and stochastic modeling.

Informació bibliogràfica

TítolSimulation and the Monte Carlo Method
Volum 707 de Wiley Series in Probability and Statistics
AutorsReuven Y. Rubinstein, Dirk P. Kroese
Edició2, il·lustrada
EditorJohn Wiley & Sons, 2011
ISBN1118210522, 9781118210529
Nre. de pàgines372 pàgines
  
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