Simulation and the Monte Carlo Method

By Dirk P Kroese, Reuven Y. Rubinstein, Dirk P. Kroese

This accessible new edition explores the major topics in Monte Carlo simulation that have arisen over the past 30 years and presents a sound foundation for problem solving Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the state-of-the-art theory, methods and applications that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century 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 importance (re-)sampling, and the transform likelihood ratio 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 for rare events estimation and combinatorial optimization, and application of Monte Carlo techniques for counting problems. An extensive range of exercises is provided at the end of each chapter, as well as a generous sampling of applied examples. The Third Edition features a new chapter on the highly versatile splitting method, with applications to rare-event estimation, counting, sampling, and optimization. A second new chapter introduces the stochastic enumeration method, which is a new fast sequential Monte Carlo method for tree search. In addition, the Third Edition features new material on - Random number generation, including multiple-recursive generators and the Mersenne Twister - Simulation of Gaussian processes, Brownian motion, and diffusion processes - Multilevel Monte Carlo method - New enhancements of the cross-entropy (CE) method, including the "improved" CE method, which uses sampling from the zero-variance distribution to find the optimal importance sampling parameters - Over 100 algorithms in modern pseudo code with flow control - Over 25 new exercises Simulation and the Monte Carlo Method, Third Edition is an excellent text for upper-undergraduate and beginning graduate courses in stochastic 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. Reuven Y. Rubinstein, DSc, was Professor Emeritus in the Faculty of Industrial Engineering and Management at Technion-Israel Institute of Technology. He 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 was 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 a Professor of Mathematics and Statistics in the School of Mathematics and Physics of The University of Queensland, Australia. He has published over 100 articles and four books in a wide range of areas in applied probability and statistics, including Monte Carlo methods, cross-entropy, randomized algorithms, tele-traffic c theory, reliability, computational statistics, applied probability, and stochastic modeling .

• Format: Hardcover
• Language: English
• ISBN: 1118632168
• ISBN13: 9781118632161
• Release Date: November 2016
• Publisher: Wiley
• Length: 432 Pages
• Weight: 1.55 lbs.
• Dimensions: 1.1" x 6.4" x 9.3"

Customer Reviews

Computer Simulation in the Next Decade

Difficult computational problems often require solutions which adapt to the problem being solved. Such sequential methods are the focus of Simulation and the Monte Carlo Method, providing an algorithmic approach to hard counting and optimization problems, the simulation of rare-event probabilities through minimum cross-entropy, sensitivity analysis, and Markov Chain Monte Carlo. This book, by two leading experts in the field, travels well-beyond the usual introduction to stochastic simulation and variance reduction to the heart of the adaptive tools required by the complex simulation and optimization problems of the next decade. I recommend the book for researchers and practitioners alike, interested in the extraordinary power and potential of modern Monte Carlo Methods for solving problems in modeling, statistics and optimization.

excellent

This is an excellent textbook for a course on stochastic simulation for senior and master students in science. It gives a comprehensive treatment of all important aspects of dynamic discrete event simulations with examples and applications in queueing and reliability models. And each chapter concludes with many problems. In this respect it is self-contained as it has a chapter on (basic principles of) probability as well. Just a minor criticism is that the book handles traditional simulation topics such as building simulation models and verification/validation rather sketchy (in chapter 3). However, there are many other topics that you quite often do not see in books on simulation, like MCMC, optimization, rare-event simulation, cross-entropy algorithms for combinatorial optimization. The authors treat the mathematical background and details before giving the simulation algorithms, which makes the book easy to use as a reference and suitable for instruction and case studies. Specifically, I enjoyed reading the last chapter on counting problems and how to solve them (approximately) by Monte Carlo simulation. There seems to be many open problems in that area and this chapter is a good starting point for initiating interesting research.

Enthusiastic reader

This book is supposed to be a revision of the classical book by Rubinstein 1981. As is pointed out in the Preface: "Dramatic changes have taken place in the entire field of Monte Carlo simulation [since 1981]". This edition includes a considerable amount of new, and important, material for which the authors were among principal developers. This alone makes this book a valuable addition to the recent literature on theory and applications of Monte Carlo methods. The book is written in a clear style and is a pleasure to read.

Up to date

This book is a revision of the classic first edition and is authoritative and up to date, including most of the interesting new advances in Monte Carlo methods including modern techniques like perfect sampling and Hit-and-Run algorithms.