Random number generation and Monte Carlo methods

Statistics and computing

James E. Gentle

396 pages, parution le 21/07/2003 (2^eme édition)

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Résumé

Monte Carlo simulation has become one of the most important tools in all fields of science. Simulation methodology relies on a good source of numbers that appear to be random. These "pseudorandom" numbers must pass statistical tests just as random samples would. Methods for producing pseudorandom numbers and transforming those numbers to simulate samples from various distributions are among the most important topics in statistical computing.

This book surveys techniques of random number generation and the use of random numbers in Monte Carlo simulation. The book covers basic principles, as well as newer methods such as parallel random number generation, nonlinear congruential generators, quasi Monte Carlo methods, and Markov chain Monte Carlo. The best methods for generating random variates from the standard distributions are presented, but also general techniques useful in more complicated models and in novel settings are described. The emphasis throughout the book is on practical methods that work well in current computing environments.

The book includes exercises and can be used as a test or supplementary text for various courses in modern statistics. It could serve as the primary test for a specialized course in statistical computing, or as a supplementary text for a course in computational statistics and other areas of modern statistics that rely on simulation. The book, which covers recent developments in the field, could also serve as a useful reference for practitioners. Although some familiarity with probability and statistics is assumed, the book is accessible to a broad audience.

The second edition is approximately 50% longer than the first edition. It includes advances in methods for parallel random number generation, universal methods for generation of nonuniform variates, perfect sampling, and software for random number generation.

Simulating Random Numbers from a Uniform Distribution
Quality of Random Number Generators
Quasirandom Numbers
Transformations of Uniform Deviates: General Methods
Simulating Random Numbers from Specific Distributions
Generation of Random Samples, Permutations, and Stochastic Processes
Monte Carlo Methods
Software for Random Number Generation
Monte Carlo Studies in Statistics

Appendices
Bibliography
Subject Index

L'auteur - James E. Gentle

James Gentle is University Professor of Computational Statistics at George Mason University. He is a fellow of the American Statistical Association (ASA)and a member of the International Statistical Institute. He has held several national offices in the American Statistical Association and has served as associate editor for journals of the ASA as well as for other journals in statistics and computing. He is the author of Random Number Generation and Monte Carlo Methods and Numerical Linear Algebra for Applications in Statistics.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	James E. Gentle
Parution	21/07/2003
Édition	2^eme édition
Nb. de pages	396
Format	16 x 24
Couverture	Relié
Poids	690g
Intérieur	Noir et Blanc
EAN13	9780387001784
ISBN13	978-0-387-00178-4