Advanced Markov chain Monte Carlo methods : learning from past samples / Faming Liang, Chuanhai Liu, Raymond J. Carroll.

Format:

Book

Author/Creator:

Liang, F. (Faming), 1970-

Contributor:

Liu, Chuanhai, 1959-

Carroll, Raymond J.

Series:

Wiley series in computational statistics.

Wiley Series in Computational Statistics

Language:

English

Subjects (All):

Monte Carlo method.

Markov processes.

Physical Description:

1 online resource (379 p.)

Place of Publication:

Hoboken, NJ : Wiley, 2010.

Language Note:

English

Summary:

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 discus

Contents:

Advanced Markov Chain Monte Carlo Methods; Contents; Preface; Acknowledgments; Publisher's Acknowledgments; 1 Bayesian Inference and Markov Chain Monte Carlo; 2 The Gibbs Sampler; 3 The Metropolis-Hastings Algorithm; 4 Auxiliary Variable MCMC Methods; 5 Population-Based MCMC Methods; 6 Dynamic Weighting; 7 Stochastic Approximation Monte Carlo; 8 Markov Chain Monte Carlo with Adaptive Proposals; References; Index

Notes:

Description based upon print version of record.

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9786612661563

9781119956808

1119956803

9781282661561

1282661566

9780470669723

0470669721

9780470669730

047066973X

OCLC:

654805877