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 book is...
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2010
|
| Schriftenreihe: | Cambridge series on statistical and probabilistic mathematics
28 |
| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (viii, 299 pages) |
| ISBN: | 9780511802478 |
| DOI: | 10.1017/CBO9780511802478 |
Internformat
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| 500 | |a Title from publisher's bibliographic system (viewed on 05 Oct 2015) | ||
| 505 | 8 | |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 | |
| 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 | ||
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Datensatz im Suchindex
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| author2_role | edt |
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| building | Verbundindex |
| bvnumber | BV043941065 |
| collection | ZDB-20-CBO |
| 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 |
| ctrlnum | (ZDB-20-CBO)CR9780511802478 (OCoLC)839014056 (DE-599)BVBBV043941065 |
| dewey-full | 519.5/42 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/42 |
| dewey-search | 519.5/42 |
| dewey-sort | 3519.5 242 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1017/CBO9780511802478 |
| format | Electronic eBook |
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| id | DE-604.BV043941065 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:14Z |
| institution | BVB |
| isbn | 9780511802478 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029350034 |
| oclc_num | 839014056 |
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| owner_facet | DE-12 DE-92 |
| physical | 1 online resource (viii, 299 pages) |
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| publishDate | 2010 |
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| publisher | Cambridge University Press |
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| series2 | Cambridge series on statistical and probabilistic mathematics |
| spelling | Bayesian nonparametrics edited by Nils Lid Hjort [and others] Cambridge Cambridge University Press 2010 1 online resource (viii, 299 pages) txt rdacontent c rdamedia cr rdacarrier Cambridge series on statistical and probabilistic mathematics 28 Title from publisher's bibliographic system (viewed on 05 Oct 2015) 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 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 Nonparametric statistics Bayesian statistical decision theory Nichtparametrische Statistik (DE-588)4226777-8 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 s Nichtparametrische Statistik (DE-588)4226777-8 s 1\p DE-604 Hjort, Nils Lid 1953- (DE-588)137124562 edt Erscheint auch als Druckausgabe 978-0-521-51346-3 https://doi.org/10.1017/CBO9780511802478 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Bayesian nonparametrics 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 Nonparametric statistics Bayesian statistical decision theory Nichtparametrische Statistik (DE-588)4226777-8 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4226777-8 (DE-588)4144220-9 |
| title | Bayesian nonparametrics |
| title_auth | Bayesian nonparametrics |
| title_exact_search | Bayesian nonparametrics |
| title_full | Bayesian nonparametrics edited by Nils Lid Hjort [and others] |
| title_fullStr | Bayesian nonparametrics edited by Nils Lid Hjort [and others] |
| title_full_unstemmed | Bayesian nonparametrics edited by Nils Lid Hjort [and others] |
| title_short | Bayesian nonparametrics |
| title_sort | bayesian nonparametrics |
| topic | Nonparametric statistics Bayesian statistical decision theory Nichtparametrische Statistik (DE-588)4226777-8 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | Nonparametric statistics Bayesian statistical decision theory Nichtparametrische Statistik Bayes-Entscheidungstheorie |
| url | https://doi.org/10.1017/CBO9780511802478 |
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