LEADER 03984nam 22006612 450 001 9910465285103321 005 20151005020622.0 010 $a1-107-20607-3 010 $a1-283-06796-X 010 $a9786613067968 010 $a0-511-67417-1 010 $a0-511-67536-4 010 $a0-511-67211-X 010 $a0-511-80247-1 010 $a0-511-67083-4 010 $a0-511-67338-8 035 $a(CKB)2560000000071437 035 $a(EBL)487278 035 $a(OCoLC)714567146 035 $a(SSID)ssj0000467162 035 $a(PQKBManifestationID)11277278 035 $a(PQKBTitleCode)TC0000467162 035 $a(PQKBWorkID)10466874 035 $a(PQKB)11085911 035 $a(UkCbUP)CR9780511802478 035 $a(MiAaPQ)EBC487278 035 $a(Au-PeEL)EBL487278 035 $a(CaPaEBR)ebr10466312 035 $a(CaONFJC)MIL306796 035 $a(EXLCZ)992560000000071437 100 $a20101021d2010|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aBayesian nonparametrics /$fedited by Nils Lid Hjort [and others]$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2010. 215 $a1 online resource (viii, 299 pages) $cdigital, PDF file(s) 225 1 $aCambridge series on statistical and probabilistic mathematics ;$v28 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-51346-4 320 $aIncludes bibliographical references and indexes. 327 $aAn invitation to Bayesian nonparametrics / Nils Lid Hjort, Chris Holmes, Peter Mu?ller and Stephen G. Walker -- 1. Bayesian nonparametric methods: motivation and ideas / Stephen G. Walker -- 2. The Dirichlet process, related priors, and posterior asymptotics / Subhashis Ghosal -- 3. Models beyond the Dirichlet process / Antonio Lijoi and Igor Pru?nster -- 4. Further models and applications / Nils Lid Hjort -- 5. Hierarchical Bayesian nonparametric models with applications / Yee Whye Teh and Michael I. Jordan -- 6. Computational issues arising in Bayesian nonparametric hierarchical models / Jim Griffin and Chris Holmes -- 7. Nonparametric Bayes applications to biostatistics / David B. Dunson -- 8. More nonparametric Bayesian models for biostatistics / Peter Mu?ller and Fernando Quintana -- Author index -- Subject index. 330 $aBayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Pru?nster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics. 410 0$aCambridge series on statistical and probabilistic mathematics ;$v28. 606 $aNonparametric statistics 606 $aBayesian statistical decision theory 615 0$aNonparametric statistics. 615 0$aBayesian statistical decision theory. 676 $a519.5/42 702 $aHjort$b Nils Lid 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910465285103321 996 $aBayesian nonparametrics$92466789 997 $aUNINA