This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems. It contains standard material usually considered in Monte Carlo simulation as well as new material such as variance reduction techniques, regenerative simulation, and Monte Carlo optimization.
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"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)
"..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)
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:
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.
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