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Bayesian nonparametrics /

"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 b...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Hjort, Nils Lid
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, UK ; New York : Cambridge University Press, 2010.
Colección:Cambridge series on statistical and probabilistic mathematics ; 28.
Temas:
Acceso en línea:Texto completo

MARC

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245 0 0 |a Bayesian nonparametrics /  |c edited by Nils Lid Hjort [and others]. 
260 |a Cambridge, UK ;  |a New York :  |b Cambridge University Press,  |c 2010. 
300 |a 1 online resource (viii, 299 pages) :  |b illustrations 
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490 1 |a Cambridge series in statistical and probabilistic mathematics ;  |v 28 
504 |a Includes bibliographical references (pages 290-291) and indexes. 
520 |a "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"--Provided by publisher 
505 0 |a 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. 
588 0 |a Print version record. 
546 |a English. 
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650 0 |a Nonparametric statistics. 
650 0 |a Bayesian statistical decision theory. 
650 2 |a Statistics, Nonparametric 
650 6 |a Statistique non paramétrique. 
650 6 |a Théorie de la décision bayésienne. 
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650 7 |a Bayesian statistical decision theory  |2 fast 
650 7 |a Nonparametric statistics  |2 fast 
700 1 |a Hjort, Nils Lid. 
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830 0 |a Cambridge series on statistical and probabilistic mathematics ;  |v 28. 
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