E-Books →Advanced Markov Chain Monte Carlo Methods Learning from Past Samples

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.This book can be used as a textbook or a reference book for a one-semester graduate course in statistics, computational biology, engineering, and computer sciences. Applied or theoretical researchers will also find this book beneficial.