By Faming Liang,Chuanhai Liu,Raymond Carroll
- Expanded insurance of the stochastic approximation Monte Carlo and dynamic weighting algorithms which are basically resistant to neighborhood seize problems.
- A certain 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 booklet can be utilized as a textbook or a reference publication for a one-semester graduate path in records, computational biology, engineering, and laptop sciences. utilized or theoretical researchers also will locate this booklet beneficial.
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Additional info for Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples (Wiley Series in Computational Statistics)
Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples (Wiley Series in Computational Statistics) by Faming Liang,Chuanhai Liu,Raymond Carroll