000			03176nam a22003618i 4500
001			CR9780511802478
003			UkCbUP
005			20200124160226.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			101021s2010||||enk o ||1 0|eng|d
020			_a9780511802478 (ebook)
020			_z9780521513463 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA278.8 _b.B39 2010
082	0	0	_a519.5/42 _222
245	0	0	_aBayesian nonparametrics / _cedited by Nils Lid Hjort [and others].
264		1	_aCambridge : _bCambridge University Press, _c2010.
300			_a1 online resource (viii, 299 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aCambridge series on statistical and probabilistic mathematics ; _v28
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0		_aAn 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.
520			_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 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.
650		0	_aNonparametric statistics.
650		0	_aBayesian statistical decision theory.
700	1		_aHjort, Nils Lid, _eeditor.
776	0	8	_iPrint version: _z9780521513463
830		0	_aCambridge series on statistical and probabilistic mathematics ; _v28.
856	4	0	_uhttps://doi.org/10.1017/CBO9780511802478
999			_c517156 _d517154