Advanced Markov Chain Monte Carlo Methods

Learning from Past Samples

Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Advanced Markov Chain Monte Carlo Methods by Faming Liang, Chuanhai Liu, Raymond Carroll, Wiley
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Author: Faming Liang, Chuanhai Liu, Raymond Carroll ISBN: 9781119956808
Publisher: Wiley Publication: July 5, 2011
Imprint: Wiley Language: English
Author: Faming Liang, Chuanhai Liu, Raymond Carroll
ISBN: 9781119956808
Publisher: Wiley
Publication: July 5, 2011
Imprint: Wiley
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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:

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.

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