By Faming Liang,Chuanhai Liu,Raymond Carroll
- Expanded assurance of the stochastic approximation Monte Carlo and dynamic weighting algorithms which are basically proof against neighborhood capture problems.
- A specific 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 direction in records, computational biology, engineering, and laptop sciences. utilized or theoretical researchers also will locate this ebook beneficial.
Read or Download Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples (Wiley Series in Computational Statistics) PDF
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