Advanced Markov Chain Monte Carlo

Faming Liang

ISBN 10: 0470748265 / ISBN 13: 9780470748268

Editore: Wiley Aug 2010, 2010

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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.

Codice articolo 9780470748268

Dati bibliografici

• Titolo: Advanced Markov Chain Monte Carlo
• Editore: Wiley Aug 2010
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 0470748265
• ISBN 13: 9780470748268
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 235 L x 157 A x 27 P (millimetri) 235 millimetri di larghezza per 157 millimetri di altezza per 27 millimetri di profondità
• Peso: 765 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.