Advanced Markov Chain Monte Carlo Methods: Learning from - download pdf or read online

By Faming Liang,Chuanhai Liu,Raymond Carroll

Markov Chain Monte Carlo (MCMC) tools are actually an critical instrument in medical computing. This publication discusses fresh advancements of MCMC tools with an emphasis on these employing prior pattern info in the course of simulations. the applying examples are drawn from different fields resembling bioinformatics, laptop studying, social technology, combinatorial optimization, and computational physics.

Key Features:

  • Expanded assurance of the stochastic approximation Monte Carlo and dynamic weighting algorithms which are basically proof against neighborhood capture problems.
  • A distinctive 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 bills 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 publication for a one-semester graduate path in data, computational biology, engineering, and computing device sciences. utilized or theoretical researchers also will locate this booklet 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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