Bayesian Nonparametrics
Bayesian Nonparametrics
Chris Holmes, University of Oxford
Peter Müller, University of Texas, M. D. Anderson Cancer Center
Stephen G. Walker, University of Kent, Canterbury
$99.00
USDBayesian 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.
- The first book to give a genuine introduction to Bayesian nonparametrics
- Detailed reviews of four of the most important areas in the field
- Explains the theoretical background as well as modern applications using real data
Reviews & endorsements
"The book looks like it will be useful to a wide range of researchers. I like that there is a lot of discussion of the models themselves as well as the computation. The book, especially in the early chapters, is more theoretical than I would prefer... But, hey, that's just my taste... on the whole I think the book is excellent. If I didn't think the book was important, I wouldn't be spending my time pointing out my disagreements with it!"
Andrew Gelman, Columbia University
"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
Product details
April 2010Hardback
9780521513463
308 pages
254 × 178 × 23 mm
0.73kg
24 b/w illus.
Available
Table of Contents
- 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.
