03984nam 22006612 450 991046528510332120151005020622.01-107-20607-31-283-06796-X97866130679680-511-67417-10-511-67536-40-511-67211-X0-511-80247-10-511-67083-40-511-67338-8(CKB)2560000000071437(EBL)487278(OCoLC)714567146(SSID)ssj0000467162(PQKBManifestationID)11277278(PQKBTitleCode)TC0000467162(PQKBWorkID)10466874(PQKB)11085911(UkCbUP)CR9780511802478(MiAaPQ)EBC487278(Au-PeEL)EBL487278(CaPaEBR)ebr10466312(CaONFJC)MIL306796(EXLCZ)99256000000007143720101021d2010|||| uy| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierBayesian nonparametrics /edited by Nils Lid Hjort [and others][electronic resource]Cambridge :Cambridge University Press,2010.1 online resource (viii, 299 pages) digital, PDF file(s)Cambridge series on statistical and probabilistic mathematics ;28Title from publisher's bibliographic system (viewed on 05 Oct 2015).0-521-51346-4 Includes bibliographical references and indexes.An invitation to Bayesian nonparametrics / Nils Lid Hjort, Chris Holmes, Peter Müller 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 Prünster -- 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 Müller and Fernando Quintana -- Author index -- Subject index.Bayesian 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 Prünster, 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.Cambridge series on statistical and probabilistic mathematics ;28.Nonparametric statisticsBayesian statistical decision theoryNonparametric statistics.Bayesian statistical decision theory.519.5/42Hjort Nils LidUkCbUPUkCbUPBOOK9910465285103321Bayesian nonparametrics2466789UNINA