Description: 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.
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EAN: 9780521513463
UPC: 9780521513463
ISBN: 9780521513463
MPN: N/A
Item Length: 25.4 cm
Number of Pages: 308 Pages
Publication Name: Bayesian Nonparametrics
Language: English
Publisher: Cambridge University Press
Item Height: 254 mm
Subject: Mathematics
Publication Year: 2010
Type: Textbook
Item Weight: 730 g
Author: Nils Lid Hjort, Chris Holmes, Peter Muller, Stephen G. Walker
Item Width: 178 mm
Format: Hardcover
