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
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.
- 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
Product details
No date availableHardback
9780521513463
308 pages
254 × 178 × 23 mm
0.73kg
24 b/w illus.
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.
