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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, UK ; New York :
Cambridge University Press,
2011.
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| Colección: | Cambridge books online.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Bayesian time series models / |c edited by David Barber, A. Taylan Cemgil, Silvia Chiappa. |
| 260 | |a Cambridge, UK ; |a New York : |b Cambridge University Press, |c 2011. | ||
| 300 | |a 1 online resource (xiii, 417 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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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. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Time-series analysis. | |
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 6 | |a Série chronologique. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 7 | |a COMPUTERS |x Computer Vision & Pattern Recognition. |2 bisacsh | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 650 | 7 | |a Time-series analysis |2 fast | |
| 700 | 1 | |a Barber, David, |d 1968- | |
| 700 | 1 | |a Cemgil, Ali Taylan. | |
| 700 | 1 | |a Chiappa, Silvia. | |
| 776 | 0 | 8 | |i Print version: |t Bayesian time series models. |d Cambridge, UK ; New York : Cambridge University Press, ©2011 |w (OCoLC)71081592 |
| 830 | 0 | |a Cambridge books online. | |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=369448 |z Texto completo |
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