Bayesian Nonparametrics

The most intelligent guide to the hottest field in statistics.

Nils Lid Hjort (Edited by), Chris Holmes (Edited by), Peter Müller (Edited by), Stephen G. Walker (Edited by)

9780521513463, Cambridge University Press

Hardback, published 12 April 2010

308 pages, 24 b/w illus.
25.4 x 17.8 x 2.3 cm, 0.73 kg

"The book provides a tour de force presentation of selected topics in an emerging branch of modern statistical science, and not only justfies the reader’s curiosity, but also expands it.... The book brings together a well-structured account of a number of topics on the theory, methodology, applications, and challenges of future developments in the rapidly expanding area of Bayesian nonparametrics. Given the current dearth of books on BNP, this book will be an invaluable source of information and reference for anyone interested in BNP, be it a student, an established statistician, or a researcher in need of flexible statistical analyses."
Milovan Krnjajic, Journal of the American Statistical Association

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.

An invitation to Bayesian nonparametrics Nils Lid Hjort, Chris Holmes, Peter Müller and Stephen G. Walker
1. Bayesian nonparametric methods: motivation and ideas Stephen G. Walker
2. The Dirichlet process, related priors, and posterior asymptotics Subhashis Ghosal
3. Models beyond the Dirichlet process Antonio Lijoi and Igor Prünster
4. Further models and applications Nils Lid Hjort
5. Hierarchical Bayesian nonparametric models with applications Yee Whye Teh and Michael I. Jordan
6. Computational issues arising in Bayesian nonparametric hierarchical models Jim Griffin and Chris Holmes
7. Nonparametric Bayes applications to biostatistics David B. Dunson
8. More nonparametric Bayesian models for biostatistics Peter Müller and Fernando Quintana
Author index
Subject index.

Subject Areas: Probability & statistics [PBT]