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Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples
Faming Liang
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Description for Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples
Hardcover. * Presents the latest developments in Monte Carlo research. * Provides a toolkit for simulating complex systems using MCMC. * Introduces a wide range of algorithms including Gibbs sampler, Metropolis-Hastings and an overview of sequential Monte Carlo algorithms. Series: Wiley Series in Computational Statistics. Num Pages: 378 pages, Illustrations. BIC Classification: PBKS. Category: (P) Professional & Vocational. Dimension: 159 x 233 x 26. Weight in Grams: 724.
Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics.
Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics.
Key Features:
- Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems.
- A detailed discussion of the Monte Carlo Metropolis-Hastings algorithm that can be used for sampling from distributions with intractable normalizing constants.
- Up-to-date accounts of ... Read more
- Comprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals.
This book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial.
Show LessProduct Details
Format
Hardback
Publication date
2010
Publisher
John Wiley & Sons Inc United Kingdom
Number of pages
378
Condition
New
Series
Wiley Series in Computational Statistics
Number of Pages
384
Place of Publication
New York, United States
ISBN
9780470748268
SKU
V9780470748268
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50
About Faming Liang
Faming Liang, Associate Professor, Department of Statistics, Texas A&M University. Chuanhai Liu, Professor, Department of Statistics, Purdue University. Raymond J. Carroll, Distinguished Professor, Department of Statistics, Texas A&M University.
Reviews for Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples
"The book is suitable as a textbook for one-semester courses on Monte Carlo methods, offered at the advance postgraduate levels." (Mathematical Reviews, 1 December 2012) "Researchers working in the field of applied statistics will profit from this easy-to-access presentation. Further illustration is done by discussing interesting examples and relevant applications. The valuable reference list includes technical reports which ... Read more