000			01808cam a2200229 a 4500
008			090911s2010 enka b 001 0 eng
020			_a9780521513463
020			_a0521513464
040			_aSDU
050	0	0	_aQA278.8 _bB357 2010
245	0	0	_aBayesian nonparametrics / _cedited by Nils Lid Hjort ... [et al.].
260			_aCambridge, UK ; _aNew York : _bCambridge University Press, _c2010.
300			_a299 p. : _bill.
490	1		_aCambridge series in statistical and probabilistic mathematics ; _v28
505			_aHKBU Library
520			_a"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"--Provided by publisher.
650		0	_aNonparametric statistics. _9114573
650		0	_aBayesian statistical decision theory.
700	1		_aHjort, Nils Lid. _9193290
900			_a = C.1 SDU
942			_cGBE _2lcc
999			_c104017 _d104017