Bayesian Nonparametrics (Hardcover)

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 Prunster, 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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Series:	Cambridge Series in Statistical and Probabilistic Mathematics
Release date:	April 2010
First published:	May 2010
Editors:	Nils Lid Hjort • Chris Holmes • Peter Muller • Stephen G. Walker
Dimensions:	254 x 178 x 23mm (L x W x T)
Format:	Hardcover
Pages:	308
ISBN-13:	978-0-521-51346-3
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics
LSN:	0-521-51346-4
Barcode:	9780521513463

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