LEADER 03437nam  22007694a 450 
001 9910814602303321
005 20240515182010.0
010   $a1-281-88113-9
010   $a9786611881139
010   $a981-270-091-9
024 3 $a9789812564276
035   $a(CKB)1000000000334250
035   $a(EBL)296164
035   $a(OCoLC)228169093
035   $a(SSID)ssj0000198227
035   $a(PQKBManifestationID)11181006
035   $a(PQKBTitleCode)TC0000198227
035   $a(PQKBWorkID)10169562
035   $a(PQKB)10293393
035   $a(MiAaPQ)EBC296164
035   $a(WSP)00001874    
035   $a(Au-PeEL)EBL296164
035   $a(CaPaEBR)ebr10173931
035   $a(CaONFJC)MIL188113
035   $a(PPN)166611751
035   $a(EXLCZ)991000000000334250
100   $a20060926d2005      uy         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 00$aMarkov chain Monte Carlo $einnovations and applications /$fedited by W.S. Kendall, F. Liang, J.-S. Wang
205   $a1st ed.
210   $aSingapore ;$aHackensack, NJ $cWorld Scientific$dc2005
215   $a1 online resource (239 p.)
225 1 $aLecture notes series, Institute for Mathematical Sciences, National University of Singapore ;$vvol. 7
300   $aDescription based upon print version of record.
311   $a981-256-427-6 
320   $aIncludes bibliographical references and index.
327   $aCONTENTS; Foreword; Preface; Glossary; Introduction to Markov Chain Monte Carlo Simulations and Their Statistical Analysis B. A. Berg; An Introduction to Monte Carlo Methods in Statistical Physics D. P. Landau; Notes on Perfect Simulation W.S. Kendall; Sequential Monte Carlo Methods and Their Applications R. Chen; MCMC in the Analysis of Genetic Data on Pedigrees E. A. Thompson; Index
330   $aMarkov Chain Monte Carlo (MCMC) originated in statistical physics, but has spilled over into various application areas, leading to a corresponding variety of techniques and methods. That variety stimulates new ideas and developments from many different places, and there is much to be gained from cross-fertilization. This book presents five expository essays by leaders in the field, drawing from perspectives in physics, statistics and genetics, and showing how different aspects of MCMC come to the fore in different contexts. The essays derive from tutorial lectures at an interdisciplinary progr
410  0$aLecture notes series (National University of Singapore. Institute for Mathematical Sciences) ;$vv. 7.
606   $aMonte Carlo method
606   $aBayesian statistical decision theory
606   $aMarkov processes
606   $aMarkov-processen$2gtt
606   $aMonte Carlo-methode$2gtt
606   $aSimulatiemodellen$2gtt
615  0$aMonte Carlo method.
615  0$aBayesian statistical decision theory.
615  0$aMarkov processes.
615 17$aMarkov-processen.
615 17$aMonte Carlo-methode.
615 17$aSimulatiemodellen.
676   $a519.233
686   $a31.80$2bcl
701   $aKendall$b W. S$0251594
701   $aLiang$b F$g(Faming),$f1970-$0522160
701   $aWang$b J.-S$g(Jian-Sheng),$f1960-$01628010
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910814602303321
996   $aMarkov chain Monte Carlo$93964878
997   $aUNINA