02966nam 2200673Ia 450 991014055680332120200520144314.097866126615639781119956808111995680397812826615611282661566978047066972304706697219780470669730047066973X(CKB)2670000000031663(EBL)555049(SSID)ssj0000411019(PQKBManifestationID)11309167(PQKBTitleCode)TC0000411019(PQKBWorkID)10352724(PQKB)11300494(MiAaPQ)EBC555049(OCoLC)654805877(Perlego)1011188(EXLCZ)99267000000003166320100414d2010 uy 0engur|n|---|||||txtccrAdvanced Markov chain Monte Carlo methods learning from past samples /Faming Liang, Chuanhai Liu, Raymond J. CarrollHoboken, NJ Wiley20101 online resource (379 p.)Wiley Series in Computational StatisticsDescription based upon print version of record.9780470748268 0470748265 Includes bibliographical references and index.Advanced Markov Chain Monte Carlo Methods; Contents; Preface; Acknowledgments; Publisher's Acknowledgments; 1 Bayesian Inference and Markov Chain Monte Carlo; 2 The Gibbs Sampler; 3 The Metropolis-Hastings Algorithm; 4 Auxiliary Variable MCMC Methods; 5 Population-Based MCMC Methods; 6 Dynamic Weighting; 7 Stochastic Approximation Monte Carlo; 8 Markov Chain Monte Carlo with Adaptive Proposals; References; IndexMarkov 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, social science, combinatorial optimization, and computational physics. Key Features:Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems.A detailed discusWiley series in computational statistics.Monte Carlo methodMarkov processesMonte Carlo method.Markov processes.518/.282Liang F(Faming),1970-522160Liu Chuanhai1959-522161Carroll Raymond J102941MiAaPQMiAaPQMiAaPQBOOK9910140556803321Advanced Markov chain Monte Carlo methods835671UNINA