Simulation and the Monte Carlo Method / Edition 2

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About the Author:
Dirk P. Kroese University of Queensland Department of Mathematics Brisbane, Australia

About the Author:
Thomas Taimre University of Queensland Department of Mathematics Brisbane, Australia

About the Author:
Zdravko I. Botev University of Queensland Department of Mathematics Brisbane, Australia

About the Author:
Reuven Y. Rubinstein Technion-Israel Institute of Technology Faculty of Industrial Engineering and Management Haifa, Israel

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Editorial Reviews

From the Publisher
"The book is clearly written and easy to read for people with mathematical background.... The material of the book is useful in most areas of the nowadays research work." (International Statistical Review, April 2009)

"I enjoyed reading the book, and found the individual examples quite interesting." (Biometrics, December 2008)

"I enjoyed reading the book, and found the individual examples quite interesting." (Biometrics, December 2008)

"..if you need to learn how to use Monte Carlo in your simulations, this is probably the best single document I have ever read. "(Computing Reviews, September 2008)

"Rubinstein and Kroese did an exemplary job of addressing major issues and providing much needed updated information in this area." (CHOICE, June 2008)

"the book is nicely written and the additional to the book from the 1st edition certainly make it more attractive to a wider audience. I would recommend it to students and practioners with appropriate background." (MAA Review March 2008)

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Product Details

Table of Contents

Preface     vii
Acknowledgments     ix
Problems     1
Preliminaries     1
Random Number, Random Variable, and Stochastic Process Generation     9
Simulation of Discrete-Event Systems     15
Statistical Analysis of Discrete-Event Systems     19
Controlling the Variance     25
Markov Chain Monte Carlo     31
Sensitivity Analysis and Monte Carlo Optimization     37
The Cross-Entropy Method     41
Counting via Monte Carlo     47
Appendix     51
Solutions     53
Preliminaries     55
Random Number, Random Variable, and Stochastic Process Generation     69
Simulation of Discrete-Event Systems     85
Statistical Analysis of Discrete-Event Systems     95
Controlling the Variance     105
Markov Chain Monte Carlo     123
Sensitivity Analysis and Monte Carlo Optimization     145
The Cross-Entropy Method     151
Counting via Monte Carlo     177
Appendix     187
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