LEADER 02797nam 2200601Ia 450 001 9910140556803321 005 20200520144314.0 010 $a1-119-95680-3 010 $a1-282-66156-6 010 $a9786612661563 010 $a0-470-66972-1 010 $a0-470-66973-X 035 $a(CKB)2670000000031663 035 $a(EBL)555049 035 $a(OCoLC)651601976 035 $a(SSID)ssj0000411019 035 $a(PQKBManifestationID)11309167 035 $a(PQKBTitleCode)TC0000411019 035 $a(PQKBWorkID)10352724 035 $a(PQKB)11300494 035 $a(MiAaPQ)EBC555049 035 $a(EXLCZ)992670000000031663 100 $a20100414d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAdvanced Markov chain Monte Carlo methods $elearning from past samples /$fFaming Liang, Chuanhai Liu, Raymond J. Carroll 210 $aHoboken, NJ $cWiley$d2010 215 $a1 online resource (379 p.) 225 1 $aWiley Series in Computational Statistics 300 $aDescription based upon print version of record. 311 $a0-470-74826-5 320 $aIncludes bibliographical references and index. 327 $aAdvanced 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; Index 330 $aMarkov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bioinformatics, machine learning, social science, combinatorial optimization, and computational physics. Key Features:Expanded coverage of the stochastic approximation Monte Carlo and dynamic weighting algorithms that are essentially immune to local trap problems.A detailed discus 410 0$aWiley series in computational statistics. 606 $aMonte Carlo method 606 $aMarkov processes 615 0$aMonte Carlo method. 615 0$aMarkov processes. 676 $a518/.282 700 $aLiang$b F$g(Faming),$f1970-$0522160 701 $aLiu$b Chuanhai$f1959-$0522161 701 $aCarroll$b Raymond J$0102941 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910140556803321 996 $aAdvanced Markov chain Monte Carlo methods$9835671 997 $aUNINA