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Advanced Markov Chain Monte Carlo Methods : Learning From Past Samples by Faming Liang, Chuanhai Liu, Raymond J. Carroll, Liang, Faming, Liu, Chuanhai, Carroll, Raymond is a nonfiction available to read on EtoBox.

What is Advanced Markov Chain Monte Carlo Methods : Learning From Past Samples about?

This book provides comprehensive coverage of simulation of complex systems using Monte Carlo methods. Developing algorithms that are immune to the local trap problem has long been considered as the most important topic in MCMC research. Various advanced MCMC algorithms which address this problem have been developed include, the modified Gibbs sampler, the methods based on auxiliary variables and the methods making use of past samples. The focus of this book is on the algorithms that make use of past samples. This book includes the multicanonical algorithm, dynamic weighting, dynamically weighted importance sampling, the Wang-Landau algorithm, equal energy sampler, stochastic approximation Monte Carlo, adaptive MCMC algorithms, conjugate gradient Monte Carlo, adaptive direction sampling, the sampling Metropolis-Hasting algorithm and the multiplica sampler Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, s

Who reads Advanced Markov Chain Monte Carlo Methods : Learning From Past Samples?

Author

Faming Liang, Chuanhai Liu, Raymond J. Carroll, Liang, Faming, Liu, Chuanhai, Carroll, Raymond

Publisher

John Wiley & Sons Ltd

Published

2010

Language

EN

ISBN

9780470669730

Category

nonfiction

Subjects

Mathematics, Stem