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Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples (Wiley Series in Computational Statistics Book 714) by Faming Liang; Chuanhai Liu; Raymond J. Carroll is a nonfiction available to read on EtoBox.

What is Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples (Wiley Series in Computational Statistics Book 714) about?

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, compu

Who reads Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples (Wiley Series in Computational Statistics Book 714)?

Author

Faming Liang; Chuanhai Liu; Raymond J. Carroll

Publisher

John Wiley & Sons Ltd

Published

2010

Language

EN

ISBN

9781282661561

Category

nonfiction

Subjects

Mathematics, Probability, Stem