Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples

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The book Advanced Markov Chain Monte Carlo Methods by Faming Liang provides an in-depth perspective on the latest developments in Markov Chain Monte Carlo (MCMC) methods, which have now become essential tools in scientific computing. This book offers a unique approach by focusing on utilizing information from previous samples during simulations, enhancing the efficiency and accuracy of these methods. Furthermore, the author discusses the applications of MCMC in various fields such as bioinformatics, machine learning, social sciences, combinatorial optimization, and computational physics.

One of the strengths of this book is its broad coverage of modern MCMC algorithms, including discussions on stochastic approximation Monte Carlo and dynamic weighting algorithms that can address local trap issues. The book also reviews classical Monte Carlo Metropolis-Hastings and Gibbs sampler algorithms, with clear explanations of their roles and applications. The MCMC population and MCMC algorithms with adaptive proposals are also explained in detail, offering a comprehensive insight for the readers.

This book is not only relevant as teaching material for graduate students in statistics, computational biology, engineering, and computer science but also serves as an important reference for applied and theoretical researchers. With its in-depth yet structured technical discussions, Advanced Markov Chain Monte Carlo Methods becomes a highly valuable guide for understanding and implementing MCMC in various scientific contexts.

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