Bayesian Nonparametrics

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.

Author: Nils Lid Hjort
Binding Type: Hardcover
Publisher: Cambridge University Press
Published: 04/12/2010
Series: Cambridge Series in Statistical and Probabilistic Mathematics #28
Pages: 308
Weight: 1.6lbs
Size: 10.00h x 7.00w x 0.90d
ISBN: 9780521513463