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 t...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, UK ; New York :
Cambridge University Press,
2011.
|
| Schriftenreihe: | Cambridge books online.
|
| Schlagworte: | |
| Online-Zugang: | 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"-- "Time series appear in a variety of disciplines, from finance to physics, computer science to biology. The origins of the subject and diverse applications in the engineering and physics literature at times obscure the commonalities in the underlying models and techniques. A central aim of this book is an attempt to make modern time series techniques accessible to a broad range of researchers, based on the unifying concept of probabilistic models. These techniques facilitate access to the modern time series literature, including financial time series prediction, video-tracking, music analysis, control and genetic sequence analysis. A particular feature of the book is that it brings together leading researchers that span the more traditional disciplines of statistics, control theory, engineering and signal processing, to the more recent area machine learning and pattern recognition"-- |
| Beschreibung: | 1 online resource (xiii, 417 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780511984679 0511984677 1139091018 9781139091015 9781139092920 1139092928 9781139091909 1139091905 1280775939 9781280775932 1107214769 9781107214767 1139092413 9781139092418 9786613686329 6613686328 |
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | 0 | |g 1. |t Inference and estimation in probabilistic time series models / |r David Barber, A. Taylan Cemgil and Silvia Chiappa -- |g I. |t Monte Carlo: |g 2. |t Adaptive Markov chain Monte Carlo: theory and methods / |r Yves Atchadé, Gersende Fort, Eric Moulines and Pierre Priouret; |g 3. |t Auxiliary particle filtering: recent developments / |r Nick Whiteley and Adam M. Johansen; |g 4. |t Monte Carlo probabilistic inference for diffusion processes: a methodological framework / |r Omiros Papaspiliopoulos -- |g II. |t Deterministic Approximations: |g 5. |t Two problems with variational expectation maximisation for time series models / |r Richard Eric Turner and Maneesh Sahani; |g 6. |t Approximate inference for continuous-time Markov processes / |r Cédric Archambeau and Manfred Opper; |g 7. |t Expectation propagation and generalised EP methods for inference in switching linear dynamical systems / |r Onno Zoeter and Tom Heskes; |g 8. |t Approximate inference in switching linear dynamical systems using Gaussian mixtures / |r David Barber -- |g III. |t Switch Models: |g 9. |t Physiological monitoring with factorial switching linear dynamical systems / |r John A. Quinn and Christopher K.I. Williams; |g 10. |t Analysis of changepoint models / |r Idris A. Eckley, Paul Fearnhead and Rebecca Killick -- |g IV. |t Multi-Object Models: |g 11. |t Approximate likelihood estimation of static parameters in multi-target models / |r Sumeetpal S. Singh, Nick Whiteley and Simon J. Godsill; |g 12. |t Sequential inference for dynamically evolving groups of objects / |r Sze Kim Pang, Simon J. Godsill, Jack Li, François Septier and Simon Hill; |g 13. |t Non-commutative harmonic analysis in multi-object tracking / |r Risi Kondor -- |g V. |t Nonparametric Models: |g 14. Markov chain Monte Carlo algorithms for Gaussian processes / |r Michalis K. Titsias, Magnus Rattray and Neil D. Lawrence; |g 15. |t Nonparametric hidden Markov models / |r Jurgen Van Gael and Zoubin Ghahramani; |g 16. |t Bayesian Gaussian process models for multi-sensor time series prediction / |r Michael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn and Nick R. Jennings -- |g VI. |t Agent-Based Models: |g 17. Optimal control theory and the linear Bellman equation / |r Hilbert J. Kappen; |g 18. |t Expectation maximisation methods for solving (PO)MDPs and optimal control problems / |r Marc Toussaint, Amos Storkey and Stefan Harmeling. |
| 520 | |a "'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"-- |c Provided by publisher. | ||
| 520 | |a "Time series appear in a variety of disciplines, from finance to physics, computer science to biology. The origins of the subject and diverse applications in the engineering and physics literature at times obscure the commonalities in the underlying models and techniques. A central aim of this book is an attempt to make modern time series techniques accessible to a broad range of researchers, based on the unifying concept of probabilistic models. These techniques facilitate access to the modern time series literature, including financial time series prediction, video-tracking, music analysis, control and genetic sequence analysis. A particular feature of the book is that it brings together leading researchers that span the more traditional disciplines of statistics, control theory, engineering and signal processing, to the more recent area machine learning and pattern recognition"-- |c Provided by publisher. | ||
| 546 | |a English. | ||
| 650 | 0 | |a Time-series analysis. |0 http://id.loc.gov/authorities/subjects/sh85135430 | |
| 650 | 0 | |a Bayesian statistical decision theory. |0 http://id.loc.gov/authorities/subjects/sh85012506 | |
| 650 | 6 | |a Série chronologique. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
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| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 650 | 7 | |a Time-series analysis |2 fast | |
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| 700 | 1 | |a Cemgil, Ali Taylan. |0 http://id.loc.gov/authorities/names/nb2008025442 | |
| 700 | 1 | |a Chiappa, Silvia. |0 http://id.loc.gov/authorities/names/n2011014866 | |
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| author2 | Barber, David, 1968- Cemgil, Ali Taylan Chiappa, Silvia |
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| author_GND | http://id.loc.gov/authorities/names/n2011014869 http://id.loc.gov/authorities/names/nb2008025442 http://id.loc.gov/authorities/names/n2011014866 |
| author_additional | David Barber, A. Taylan Cemgil and Silvia Chiappa -- Yves Atchadé, Gersende Fort, Eric Moulines and Pierre Priouret; Nick Whiteley and Adam M. Johansen; Omiros Papaspiliopoulos -- Richard Eric Turner and Maneesh Sahani; Cédric Archambeau and Manfred Opper; Onno Zoeter and Tom Heskes; David Barber -- John A. Quinn and Christopher K.I. Williams; Idris A. Eckley, Paul Fearnhead and Rebecca Killick -- Sumeetpal S. Singh, Nick Whiteley and Simon J. Godsill; Sze Kim Pang, Simon J. Godsill, Jack Li, François Septier and Simon Hill; Risi Kondor -- Michalis K. Titsias, Magnus Rattray and Neil D. Lawrence; Jurgen Van Gael and Zoubin Ghahramani; Michael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn and Nick R. Jennings -- Hilbert J. Kappen; Marc Toussaint, Amos Storkey and Stefan Harmeling. |
| author_facet | Barber, David, 1968- Cemgil, Ali Taylan Chiappa, Silvia |
| author_sort | Barber, David, 1968- |
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| callnumber-search | QA280 .B39 2011eb |
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| collection | ZDB-4-EBA |
| contents | Inference and estimation in probabilistic time series models / Monte Carlo: Adaptive Markov chain Monte Carlo: theory and methods / Auxiliary particle filtering: recent developments / Monte Carlo probabilistic inference for diffusion processes: a methodological framework / Deterministic Approximations: Two problems with variational expectation maximisation for time series models / Approximate inference for continuous-time Markov processes / Expectation propagation and generalised EP methods for inference in switching linear dynamical systems / Approximate inference in switching linear dynamical systems using Gaussian mixtures / Switch Models: Physiological monitoring with factorial switching linear dynamical systems / Analysis of changepoint models / Multi-Object Models: Approximate likelihood estimation of static parameters in multi-target models / Sequential inference for dynamically evolving groups of objects / Non-commutative harmonic analysis in multi-object tracking / Nonparametric Models: Nonparametric hidden Markov models / Bayesian Gaussian process models for multi-sensor time series prediction / Agent-Based Models: Expectation maximisation methods for solving (PO)MDPs and optimal control problems / |
| ctrlnum | (OCoLC)761399483 |
| dewey-full | 519.5/5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/5 |
| dewey-search | 519.5/5 |
| dewey-sort | 3519.5 15 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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Godsill, Jack Li, François Septier and Simon Hill;</subfield><subfield code="g">13.</subfield><subfield code="t">Non-commutative harmonic analysis in multi-object tracking /</subfield><subfield code="r">Risi Kondor --</subfield><subfield code="g">V.</subfield><subfield code="t">Nonparametric Models:</subfield><subfield code="g">14. Markov chain Monte Carlo algorithms for Gaussian processes /</subfield><subfield code="r">Michalis K. Titsias, Magnus Rattray and Neil D. Lawrence;</subfield><subfield code="g">15.</subfield><subfield code="t">Nonparametric hidden Markov models /</subfield><subfield code="r">Jurgen Van Gael and Zoubin Ghahramani;</subfield><subfield code="g">16.</subfield><subfield code="t">Bayesian Gaussian process models for multi-sensor time series prediction /</subfield><subfield code="r">Michael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn and Nick R. Jennings --</subfield><subfield code="g">VI.</subfield><subfield code="t">Agent-Based Models:</subfield><subfield code="g">17. Optimal control theory and the linear Bellman equation /</subfield><subfield code="r">Hilbert J. Kappen;</subfield><subfield code="g">18.</subfield><subfield code="t">Expectation maximisation methods for solving (PO)MDPs and optimal control problems /</subfield><subfield code="r">Marc Toussaint, Amos Storkey and Stefan Harmeling.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">"'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. 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| genre | Electronic book. |
| genre_facet | Electronic book. |
| id | ZDB-4-EBA-ocn761399483 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:18:06Z |
| institution | BVB |
| isbn | 9780511984679 0511984677 1139091018 9781139091015 9781139092920 1139092928 9781139091909 1139091905 1280775939 9781280775932 1107214769 9781107214767 1139092413 9781139092418 9786613686329 6613686328 |
| language | English |
| oclc_num | 761399483 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xiii, 417 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Cambridge University Press, |
| record_format | marc |
| series | Cambridge books online. |
| spelling | Bayesian time series models / edited by David Barber, A. Taylan Cemgil, Silvia Chiappa. Cambridge, UK ; New York : Cambridge University Press, 2011. 1 online resource (xiii, 417 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. 1. Inference and estimation in probabilistic time series models / David Barber, A. Taylan Cemgil and Silvia Chiappa -- I. Monte Carlo: 2. Adaptive Markov chain Monte Carlo: theory and methods / Yves Atchadé, Gersende Fort, Eric Moulines and Pierre Priouret; 3. Auxiliary particle filtering: recent developments / Nick Whiteley and Adam M. Johansen; 4. Monte Carlo probabilistic inference for diffusion processes: a methodological framework / Omiros Papaspiliopoulos -- II. Deterministic Approximations: 5. Two problems with variational expectation maximisation for time series models / Richard Eric Turner and Maneesh Sahani; 6. Approximate inference for continuous-time Markov processes / Cédric Archambeau and Manfred Opper; 7. Expectation propagation and generalised EP methods for inference in switching linear dynamical systems / Onno Zoeter and Tom Heskes; 8. Approximate inference in switching linear dynamical systems using Gaussian mixtures / David Barber -- III. Switch Models: 9. Physiological monitoring with factorial switching linear dynamical systems / John A. Quinn and Christopher K.I. Williams; 10. Analysis of changepoint models / Idris A. Eckley, Paul Fearnhead and Rebecca Killick -- IV. Multi-Object Models: 11. Approximate likelihood estimation of static parameters in multi-target models / Sumeetpal S. Singh, Nick Whiteley and Simon J. Godsill; 12. Sequential inference for dynamically evolving groups of objects / Sze Kim Pang, Simon J. Godsill, Jack Li, François Septier and Simon Hill; 13. Non-commutative harmonic analysis in multi-object tracking / Risi Kondor -- V. Nonparametric Models: 14. Markov chain Monte Carlo algorithms for Gaussian processes / Michalis K. Titsias, Magnus Rattray and Neil D. Lawrence; 15. Nonparametric hidden Markov models / Jurgen Van Gael and Zoubin Ghahramani; 16. Bayesian Gaussian process models for multi-sensor time series prediction / Michael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn and Nick R. Jennings -- VI. Agent-Based Models: 17. Optimal control theory and the linear Bellman equation / Hilbert J. Kappen; 18. Expectation maximisation methods for solving (PO)MDPs and optimal control problems / Marc Toussaint, Amos Storkey and Stefan Harmeling. "'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"-- Provided by publisher. "Time series appear in a variety of disciplines, from finance to physics, computer science to biology. The origins of the subject and diverse applications in the engineering and physics literature at times obscure the commonalities in the underlying models and techniques. A central aim of this book is an attempt to make modern time series techniques accessible to a broad range of researchers, based on the unifying concept of probabilistic models. These techniques facilitate access to the modern time series literature, including financial time series prediction, video-tracking, music analysis, control and genetic sequence analysis. A particular feature of the book is that it brings together leading researchers that span the more traditional disciplines of statistics, control theory, engineering and signal processing, to the more recent area machine learning and pattern recognition"-- Provided by publisher. English. Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Bayesian statistical decision theory. http://id.loc.gov/authorities/subjects/sh85012506 Série chronologique. Théorie de la décision bayésienne. COMPUTERS Computer Vision & Pattern Recognition. bisacsh Bayesian statistical decision theory fast Time-series analysis fast Electronic book. Barber, David, 1968- https://id.oclc.org/worldcat/entity/E39PBJcGkFgQRxcYTYfQVgbfMP http://id.loc.gov/authorities/names/n2011014869 Cemgil, Ali Taylan. http://id.loc.gov/authorities/names/nb2008025442 Chiappa, Silvia. http://id.loc.gov/authorities/names/n2011014866 Print version: Bayesian time series models. Cambridge, UK ; New York : Cambridge University Press, ©2011 (OCoLC)71081592 Cambridge books online. FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=369448 Volltext |
| spellingShingle | Bayesian time series models / Cambridge books online. Inference and estimation in probabilistic time series models / Monte Carlo: Adaptive Markov chain Monte Carlo: theory and methods / Auxiliary particle filtering: recent developments / Monte Carlo probabilistic inference for diffusion processes: a methodological framework / Deterministic Approximations: Two problems with variational expectation maximisation for time series models / Approximate inference for continuous-time Markov processes / Expectation propagation and generalised EP methods for inference in switching linear dynamical systems / Approximate inference in switching linear dynamical systems using Gaussian mixtures / Switch Models: Physiological monitoring with factorial switching linear dynamical systems / Analysis of changepoint models / Multi-Object Models: Approximate likelihood estimation of static parameters in multi-target models / Sequential inference for dynamically evolving groups of objects / Non-commutative harmonic analysis in multi-object tracking / Nonparametric Models: Nonparametric hidden Markov models / Bayesian Gaussian process models for multi-sensor time series prediction / Agent-Based Models: Expectation maximisation methods for solving (PO)MDPs and optimal control problems / Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Bayesian statistical decision theory. http://id.loc.gov/authorities/subjects/sh85012506 Série chronologique. Théorie de la décision bayésienne. COMPUTERS Computer Vision & Pattern Recognition. bisacsh Bayesian statistical decision theory fast Time-series analysis fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85135430 http://id.loc.gov/authorities/subjects/sh85012506 |
| title | Bayesian time series models / |
| title_alt | Inference and estimation in probabilistic time series models / Monte Carlo: Adaptive Markov chain Monte Carlo: theory and methods / Auxiliary particle filtering: recent developments / Monte Carlo probabilistic inference for diffusion processes: a methodological framework / Deterministic Approximations: Two problems with variational expectation maximisation for time series models / Approximate inference for continuous-time Markov processes / Expectation propagation and generalised EP methods for inference in switching linear dynamical systems / Approximate inference in switching linear dynamical systems using Gaussian mixtures / Switch Models: Physiological monitoring with factorial switching linear dynamical systems / Analysis of changepoint models / Multi-Object Models: Approximate likelihood estimation of static parameters in multi-target models / Sequential inference for dynamically evolving groups of objects / Non-commutative harmonic analysis in multi-object tracking / Nonparametric Models: Nonparametric hidden Markov models / Bayesian Gaussian process models for multi-sensor time series prediction / Agent-Based Models: Expectation maximisation methods for solving (PO)MDPs and optimal control problems / |
| 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 | Time-series analysis. http://id.loc.gov/authorities/subjects/sh85135430 Bayesian statistical decision theory. http://id.loc.gov/authorities/subjects/sh85012506 Série chronologique. Théorie de la décision bayésienne. COMPUTERS Computer Vision & Pattern Recognition. bisacsh Bayesian statistical decision theory fast Time-series analysis fast |
| topic_facet | Time-series analysis. Bayesian statistical decision theory. Série chronologique. Théorie de la décision bayésienne. COMPUTERS Computer Vision & Pattern Recognition. Bayesian statistical decision theory Time-series analysis Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=369448 |
| work_keys_str_mv | AT barberdavid bayesiantimeseriesmodels AT cemgilalitaylan bayesiantimeseriesmodels AT chiappasilvia bayesiantimeseriesmodels |