01710cam a2200193 a 4500008004100000020001800041020001500059040000800074050002300082245007000105260006800175300001900243490007200262505001700334520107200351650003001423650004201453700002101495090911s2010    enka     b    001 0 eng    a9780521513463  a0521513464  aSDU00aQA278.8bB357 201000aBayesian nonparametrics /cedited by Nils Lid Hjort ... [et al.].  aCambridge, UK ;aNew York :bCambridge University Press,c2010.  a299 p. :bill.1 aCambridge series in statistical and probabilistic mathematics ;v28  aHKBU Library  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. 0aNonparametric statistics. 0aBayesian statistical decision theory.1 aHjort, Nils Lid.