By Faming Liang,Chuanhai Liu,Raymond Carroll

ISBN-10: 0470748265

ISBN-13: 9780470748268

Markov Chain Monte Carlo (MCMC) equipment are actually an fundamental device in clinical computing. This ebook discusses fresh advancements of MCMC equipment with an emphasis on these utilizing previous pattern details in the course of simulations. the applying examples are drawn from various fields similar to bioinformatics, laptop studying, social technology, combinatorial optimization, and computational physics.

Key Features:

  • Expanded insurance of the stochastic approximation Monte Carlo and dynamic weighting algorithms which are basically proof against neighborhood seize problems.
  • A specified dialogue of the Monte Carlo Metropolis-Hastings set of rules that may be used for sampling from distributions with intractable normalizing constants.
  • Up-to-date money owed of modern advancements of the Gibbs sampler.
  • Comprehensive overviews of the population-based MCMC algorithms and the MCMC algorithms with adaptive proposals.

This ebook can be utilized as a textbook or a reference booklet for a one-semester graduate path in facts, computational biology, engineering, and laptop sciences. utilized or theoretical researchers also will locate this ebook beneficial.

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Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples (Wiley Series in Computational Statistics) by Faming Liang,Chuanhai Liu,Raymond Carroll


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