Advanced Markov Chain Monte Carlo Methods
Language: English
Published by John Wiley and Sons Inc, US, 2010
- Hardcover
- New
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
AbeBooks seller since June 11, 2025
Condition: New
£ 113.38
Quantity: Over 20 available
Add to basketItem description from seller
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 recent developments of the Gibbs sampler.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.…
Seller Inventory # LU-9780470748268
- Title
- Advanced Markov Chain Monte Carlo Methods
- Author
- Faming Liang, Raymond Carroll, Chuanhai Liu
- Publisher
- John Wiley and Sons Inc, US
- Publication year
- 2010
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 0470748265
- ISBN 13
- 9780470748268
- Item weight
- 737 grams
- Dimensions
- 16.25 x 2.63 x 23.25 cm
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 recent developments of the Gibbs sampler.
- 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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
Rarewaves.com UK
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