Advanced Markov Chain Monte Carlo

Faming Liang

ISBN 10: 0470748265 / ISBN 13: 9780470748268

Published by Wiley Aug 2010, 2010

About this Item

Neuware - 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.\* Accompanied by a supporting website featuring datasets used in the book, along with codes used for some simulation examples.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 # 9780470748268

Bibliographic Details

• Title: Advanced Markov Chain Monte Carlo
• Publisher: Wiley Aug 2010
• Publication Date: 2010
• Language: English
• ISBN 10: 0470748265
• ISBN 13: 9780470748268
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 157H x 27D millimeters 235 millimeters width by 157 millimeters height by 27 millimeters depth
• Weight: 765 grams

About this title

Synopsis

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.

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.