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Library | Materyal Türü | Barkod | Yer Numarası | Durum |
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Searching... Pamukkale Merkez Kütüphanesi | Kitap | 0061443 | QA298R8961 2008 | Searching... Unknown |
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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 methodAn 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.
Author Notes
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.
Reviews (1)
Choice Review
Appearing more than 25 years after the first edition, this work captures the dramatic changes that have occurred in the Monte Carlo method and simulation. Rubinstein (emer., Technion-Israel Institute of Technology) and Kroese (Univ. of Queensland, Australia) did an exemplary job of addressing major issues and providing much needed updated information in this area. Numerous examples and a very thorough bibliography are included. The book appeals to a wide array of disciplines including computer science, mathematics, statistics, and engineering. A very thorough and detailed general background of the Monte Carlo method and simulation is provided, as well as an introduction of many new important topics. The authors also discuss Monte Carlo simulation and variance reduction techniques, and the Metropolis-Hastings algorithm and Gibbs sampler. New topics such as the screening method for variance reduction, the transform likelihood ratio method, the score function for sensitivity analysis, and stochastic counterpart methods are introduced. Summing Up: Highly recommended. Upper-division undergraduates through researchers/faculty. D. J. Gougeon University of Scranton