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Random Number Generation and Monte Carlo Methods

Random Number Generation and Monte Carlo Methods BOOK REVIEWS 431 chapter for readers who do not have strong backgrounds in stochastic pro- tion and covariance matrices. This chapter will be of particular interest cess. A less advanced audience could skip this chapter for the first time. to practitioners because it focuses on clear algorithms and efficient meth- Chapters 5 (“Monte Carlo Optimization”), 6 (“The Metropolis-Hastings ods, with more complicated algorithms and specialized methods given as Algorithm”), and 7 (“The Gibbs Sampler”) are the central parts of MCMC references only. methods, which many students, practitioners, and researchers are attempt- Chapter 4 discusses the generation of random samples, both with and ing to learn about. Unlike most simulation books, authors introduce the without replacement and with fixed and random sample sizes, and the Metropolis-Hastings algorithm first and then treat the Gibbs sampler as a generation of permutations of datasets. It provides an excellent discussion special case. I think this order is more “logical” than the other way round. of why these methods strain the limits of random number generators with The authors did a fine job in separating Chapters 8 (“Diagnosing Con- finite periods (see p. 124). Finally, it discusses some special topics in the vergence”) and 9 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Technometrics Taylor & Francis

Random Number Generation and Monte Carlo Methods

B. Borkowf, Craig
Technometrics , Volume 42 (4): 2 – Nov 1, 2000
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References (2)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1537-2723
eISSN
0040-1706
DOI
10.1080/00401706.2000.10485723
Publisher site
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Abstract

BOOK REVIEWS 431 chapter for readers who do not have strong backgrounds in stochastic pro- tion and covariance matrices. This chapter will be of particular interest cess. A less advanced audience could skip this chapter for the first time. to practitioners because it focuses on clear algorithms and efficient meth- Chapters 5 (“Monte Carlo Optimization”), 6 (“The Metropolis-Hastings ods, with more complicated algorithms and specialized methods given as Algorithm”), and 7 (“The Gibbs Sampler”) are the central parts of MCMC references only. methods, which many students, practitioners, and researchers are attempt- Chapter 4 discusses the generation of random samples, both with and ing to learn about. Unlike most simulation books, authors introduce the without replacement and with fixed and random sample sizes, and the Metropolis-Hastings algorithm first and then treat the Gibbs sampler as a generation of permutations of datasets. It provides an excellent discussion special case. I think this order is more “logical” than the other way round. of why these methods strain the limits of random number generators with The authors did a fine job in separating Chapters 8 (“Diagnosing Con- finite periods (see p. 124). Finally, it discusses some special topics in the vergence”) and 9

Journal

Technometrics – Taylor & Francis

Published: Nov 1, 2000

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