Bayesian Nonparametrics

Inbunden, Engelska, 2010

Del 28 i serien Cambridge Series in Statistical and Probabilistic Mathematics

1 132 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2010-04-12
Mått:178 x 254 x 23 mm
Vikt:730 g
Format:Inbunden
Språk:Engelska
Serie:Cambridge Series in Statistical and Probabilistic Mathematics
Antal sidor:308
Förlag:Cambridge University Press
ISBN:9780521513463

Utforska kategorier

Matematisk statistik inom Naturvetenskap och teknik

Mer om författaren

Nils Lid Hjort is Professor of Mathematical Statistics in the Department of Mathematics at the University of Oslo. 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 is Professor in the Department of Biostatistics at the University of Texas M. D. Anderson Cancer Center. Stephen G. Walker is Professor of Statistics in the Institute of Mathematics, Statistics and Actuarial Science at the University of Kent, Canterbury.

Recensioner i media

"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

Innehållsförteckning

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.