Fundamentals of nonparametric Bayesian inference:
Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances...
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge ; New York, NY ; Port Melbourne ; Delhi ; Singapore
Cambridge University Press
[2017]
|
| Schriftenreihe: | Cambridge series in statistical and probabilistic mathematics
44 |
| Schlagwörter: | |
| Links: | https://doi.org/10.1017/9781139029834 https://doi.org/10.1017/9781139029834 https://doi.org/10.1017/9781139029834 https://doi.org/10.1017/9781139029834 https://doi.org/10.1017/9781139029834 |
| Zusammenfassung: | Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 11 Aug 2017) |
| Umfang: | 1 online resource (xxiv, 646 Seiten) |
| ISBN: | 9781139029834 |
| DOI: | 10.1017/9781139029834 |
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| id | DE-604.BV044508089 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T18:04:41Z |
| institution | BVB |
| isbn | 9781139029834 |
| language | English |
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| spelling | Ghosal, Subhashis Verfasser (DE-588)1132587476 aut Fundamentals of nonparametric Bayesian inference Subhashis Ghosal, North Carolina State University, Aad van der Vaart, Leiden University Cambridge ; New York, NY ; Port Melbourne ; Delhi ; Singapore Cambridge University Press [2017] © 2017 1 online resource (xxiv, 646 Seiten) txt rdacontent c rdamedia cr rdacarrier Cambridge series in statistical and probabilistic mathematics 44 Title from publisher's bibliographic system (viewed on 11 Aug 2017) Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics Nonparametric statistics Bayesian statistical decision theory Bayes-Inferenz (DE-588)4648118-7 gnd rswk-swf Statistische Schlussweise (DE-588)4182963-3 gnd rswk-swf Bayes-Inferenz (DE-588)4648118-7 s Statistische Schlussweise (DE-588)4182963-3 s 1\p DE-604 Vaart, Aad W. van der 1959- Verfasser (DE-588)114474680 aut Erscheint auch als Druck-Ausgabe, hardback 978-0-521-87826-5 (DE-604)BV044364280 Cambridge series in statistical and probabilistic mathematics 44 (DE-604)BV041460443 44 https://doi.org/10.1017/9781139029834 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Ghosal, Subhashis Vaart, Aad W. van der 1959- Fundamentals of nonparametric Bayesian inference Cambridge series in statistical and probabilistic mathematics Nonparametric statistics Bayesian statistical decision theory Bayes-Inferenz (DE-588)4648118-7 gnd Statistische Schlussweise (DE-588)4182963-3 gnd |
| subject_GND | (DE-588)4648118-7 (DE-588)4182963-3 |
| title | Fundamentals of nonparametric Bayesian inference |
| title_auth | Fundamentals of nonparametric Bayesian inference |
| title_exact_search | Fundamentals of nonparametric Bayesian inference |
| title_full | Fundamentals of nonparametric Bayesian inference Subhashis Ghosal, North Carolina State University, Aad van der Vaart, Leiden University |
| title_fullStr | Fundamentals of nonparametric Bayesian inference Subhashis Ghosal, North Carolina State University, Aad van der Vaart, Leiden University |
| title_full_unstemmed | Fundamentals of nonparametric Bayesian inference Subhashis Ghosal, North Carolina State University, Aad van der Vaart, Leiden University |
| title_short | Fundamentals of nonparametric Bayesian inference |
| title_sort | fundamentals of nonparametric bayesian inference |
| topic | Nonparametric statistics Bayesian statistical decision theory Bayes-Inferenz (DE-588)4648118-7 gnd Statistische Schlussweise (DE-588)4182963-3 gnd |
| topic_facet | Nonparametric statistics Bayesian statistical decision theory Bayes-Inferenz Statistische Schlussweise |
| url | https://doi.org/10.1017/9781139029834 |
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