Stochastic simulation
著者
書誌事項
Stochastic simulation
（Wiley series in probability and mathematical statistics）（Wiley-interscience paperback series）
J. Wiley, c2006
- : pbk
大学図書館所蔵 全13件
注記
Originally published in 1987
Includes bibliographical references (p. 200-214) and index
内容説明・目次
内容説明
WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
". . .this is a very competently written and useful addition to the statistical literature; a book every statistician should look at and that many should study!"
-Short Book Reviews, International Statistical Institute
". . .reading this book was an enjoyable learning experience. The suggestions and recommendations on the methods [make] this book an excellent reference for anyone interested in simulation. With its compact structure and good coverage of material, it [is] an excellent textbook for a simulation course."
-Technometrics
". . .this work is an excellent comprehensive guide to simulation methods, written by a very competent author. It is especially recommended for those users of simulation methods who want more than a 'cook book'. "
-Mathematics Abstracts
This book is a comprehensive guide to simulation methods with explicit recommendations of methods and algorithms. It covers both the technical aspects of the subject, such as the generation of random numbers, non-uniform random variates and stochastic processes, and the use of simulation. Supported by the relevant mathematical theory, the text contains a great deal of unpublished research material, including coverage of the analysis of shift-register generators, sensitivity analysis of normal variate generators, analysis of simulation output, and more.
目次
1 Aims of Simulation. 2 Pseudo-Random Numbers.
3 Random Variables.
4 Stochastic Models.
5 Variance Reduction.
6 Output Analysis.
7 Uses of Simulation.
Appendixes.
Index.
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