Simulation and the Monte Carlo Method

Rubinstein (industrial engineering emeritus, Technion-Israel Institute of Technology) and Kroese (statistics, U. of Queensland) take advanced undergraduates and beginning graduate students (armed with little more than a basic understanding of probability and statistics) through this rapidly expanding study in engineering, computer science and the physical and life sciences. The authors have completely updated this edition with new findings and examples, starting with basic information on such topics as distributions, transforms, Poisson processes, Markov processes and convex optimization and quality. They then explain generation of random numbers and variables, stochastic process generation, simulation of discrete-event systems and their statistical analysis, controlling the variance, Markov chain Monte Carlo, sensitivity analysis and Monte Carlo optimization, the cross-entropy methods, and counting with Monte Carlo. The authors provide exercises with every chapter and a helpful set of appendices on such topics as discrete-time Kalman filters and Bernoulli disruption problems. Annotation c2008 Book News, Inc., Portland, OR (booknews.com)

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.