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Advanced Markov chain Monte Carlo methods; learning from past samples.


Advanced Markov chain Monte Carlo methods; learning from past samples.

Liang, Faming et al.

John Wiley & Sons


357 pages



Computational statistics


Developed during the decade 1945-55 to simulate some probabilistic problems in atomic bomb designs, the Markov Chain Monte Carlo method has now become the dominant methodology for solving many classes of computational problems in science and technology. Statisticians Liang, Raymond J. Carroll (both Texas A&M U.), and Chuanhai Liu (Purdue U.) explain advanced algorithms and their variants, particularly those that involve learning from past samples in order to avoid the local-trap problem. They assume readers to be researchers specializing in Monte Carlo algorithms; scientists interested in using Monte Carlo methods; and graduate students in statistics, computational biology, engineering, and computer science who have at least a semester each of probability theory and statistical inference at the undergraduate level. Chapter-end exercises are provided.

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Publication:SciTech Book News
Article Type:Book review
Date:Dec 1, 2010
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