000			02158 a2200265 4500
003			NISER
005			20260113114956.0
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020			_a9780521878265 _qHardback
040			_aNISER LIBRARY _beng _cNISER LIBRARY
082	0	4	_a519.234 _bGHO-F
100	1		_aGhosal, Subhashis
245	1	0	_aFundamentals of nonparametric Bayesian inference
260			_aNew York : _bCambridge University Press, _c2017.
300			_axxiv, 646 pages ; _c27 cm.
490			_aCambridge series in statistical and probabilistic mathematics ; _v44
504			_aIncludes bibliographical references (pages 623-637) and indexes.
520			_aExplosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics.
650		0	_aNonparametric statistics
650		0	_aBayesian statistical decision theory
700	1		_aVaart, Aad van der
856	4	1	_3Table of contents _uhttps://toc.library.ethz.ch/objects/pdf03/z01_978-0-521-87826-5_01.pdf
856	4	1	_3Reviews _uhttps://www.goodreads.com/book/show/35485670-fundamentals-of-nonparametric-bayesian-inference?ref=nav_sb_ss_1_13#CommunityReviews
942			_cBK _2udc
999			_c36747 _d36747