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Look Inside Bayesian Nonparametrics

Bayesian Nonparametrics

£75.99

Part of Cambridge Series in Statistical and Probabilistic Mathematics

Editors:
Nils Lid Hjort, Universitetet i Oslo
Chris Holmes, University of Oxford
Peter Müller, University of Texas, M. D. Anderson Cancer Center
Stephen G. Walker, University of Kent, Canterbury

Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker, Subhashis Ghosal, Antonio Lijoi, Igor Prünster, Yee Whye Teh, Michael I. Jordan, Jim Griffin, David B. Dunson, Fernando Quintana

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Date Published: June 2010
availability: Available
format: Hardback
isbn: 9780521513463

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About the Authors

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
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Product details
- Date Published: June 2010
- format: Hardback
- isbn: 9780521513463
- length: 308 pages
- dimensions: 254 x 178 x 23 mm
- weight: 0.73kg
- contains: 24 b/w illus.
- availability: 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.
Look Inside
Editors
Nils Lid Hjort, Universitetet i Oslo
Nils Lid Hjort is Professor of Mathematical Statistics in the Department of Mathematics at the University of Oslo.
Chris Holmes, University of Oxford
Chris Holmes is Professor of Biostatistics in the Department of Statistics at the University of Oxford. He has been awarded the Guy Medal in Bronze for 2009 by the Royal Statistical Society.
Peter Müller, University of Texas, M. D. Anderson Cancer Center
Peter Müller is Professor in the Department of Biostatistics at the University of Texas M. D. Anderson Cancer Center.
Stephen G. Walker, University of Kent, Canterbury
Stephen G. Walker is Professor of Statistics in the Institute of Mathematics, Statistics and Actuarial Science at the University of Kent, Canterbury.
Contributors
Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker, Subhashis Ghosal, Antonio Lijoi, Igor Prünster, Yee Whye Teh, Michael I. Jordan, Jim Griffin, David B. Dunson, Fernando Quintana