Bayesian time series models:
'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the uni...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2011
|
| Schlagworte: | |
| Online-Zugang: | DE-12 DE-92 DE-739 Volltext |
| Zusammenfassung: | 'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xiii, 417 pages) |
| ISBN: | 9780511984679 |
| DOI: | 10.1017/CBO9780511984679 |
Internformat
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Datensatz im Suchindex
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| contents | Inference and estimation in probabilistic time series models |
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| id | DE-604.BV043942536 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-24T01:01:10Z |
| institution | BVB |
| isbn | 9780511984679 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029351506 |
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| physical | 1 online resource (xiii, 417 pages) |
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| publishDate | 2011 |
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| publisher | Cambridge University Press |
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| spelling | Bayesian time series models edited by David Barber, A. Taylan Cemgil, Silvia Chiappa Cambridge Cambridge University Press 2011 1 online resource (xiii, 417 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) Inference and estimation in probabilistic time series models David Barber, A. Taylan Cemgil and Silvia Chiappa 'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice Congrès Belge de la Route (DE-588)6-1 gnd rswk-swf Time-series analysis Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 s Congrès Belge de la Route (DE-588)6-1 f 1\p DE-604 Barber, David 1968- (DE-588)1014941148 edt Cemgil, Ali Taylan (DE-588)1064675808 edt Chiappa, Silvia (DE-588)1187367095 edt Erscheint auch als Druck-Ausgabe 978-0-521-19676-5 https://doi.org/10.1017/CBO9780511984679 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Bayesian time series models Inference and estimation in probabilistic time series models Congrès Belge de la Route (DE-588)6-1 gnd Time-series analysis Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd |
| subject_GND | (DE-588)6-1 (DE-588)4204326-8 |
| title | Bayesian time series models |
| title_alt | Inference and estimation in probabilistic time series models |
| title_auth | Bayesian time series models |
| title_exact_search | Bayesian time series models |
| title_full | Bayesian time series models edited by David Barber, A. Taylan Cemgil, Silvia Chiappa |
| title_fullStr | Bayesian time series models edited by David Barber, A. Taylan Cemgil, Silvia Chiappa |
| title_full_unstemmed | Bayesian time series models edited by David Barber, A. Taylan Cemgil, Silvia Chiappa |
| title_short | Bayesian time series models |
| title_sort | bayesian time series models |
| topic | Congrès Belge de la Route (DE-588)6-1 gnd Time-series analysis Bayesian statistical decision theory Bayes-Verfahren (DE-588)4204326-8 gnd |
| topic_facet | Congrès Belge de la Route Time-series analysis Bayesian statistical decision theory Bayes-Verfahren |
| url | https://doi.org/10.1017/CBO9780511984679 |
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