000			nam a22 7a 4500
999			_c39178 _d39178
008			190312b2017 xxu||||| |||| 00| 0 eng d
020			_a9780521878265
082			_a519.542 GHO-S
100			_aGhosal, Subhashis
245			_aFundamentals of nonparametric Bayesian inference / _cSubhashis Ghosal and Aad van der Vaart
260			_aUnited Kingdom _bCambridge University Press _c2017
300			_a646 p.
365			_aGBP _b64.00.
500			_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			_aNonparametric statistics
650			_aBayesian statistical decision theory
700			_aVaart, Aad Van Der