Bayesian time series models

Barber, David, 1968- (Added Author). Cemgil, Ali Taylan. (Added Author). Chiappa, Silvia. (Added Author).

Summary: "'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"--

Record details

ISBN: 9781139091015
ISBN: 9780521196765
ISBN: 0521196760
ISBN: 9781280775932
ISBN: 9781139092920
ISBN: 9781139091909
ISBN: 9781139091015
ISBN: 9780511984679
ISBN: 1280775939
ISBN: 1139092928
ISBN: 1139091905
ISBN: 1139091018
ISBN: 0511984677
Physical Description: 1 online resource (xiii, 417 pages)
remote
Computer data.
Publisher: Cambridge, UK ; New York : Cambridge University Press, 2011.

Content descriptions

General Note:	CatMonthString:april.19 Multi-User.
Bibliography, etc. Note:	Includes bibliographical references and index.
Formatted Contents Note:	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 Atchad�e, 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 / C�edric 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, Fran�cois 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.
Type of Computer File or Data Note:	Text (HTML), electronic book.
System Details Note:	Mode of access: Internet.
Terms Governing Use and Reproduction Note:	Access requires VIU IP addresses and is restricted to VIU students, faculty and staff. Access restricted by subscription.
Issuing Body Note:	Made available online by EBSCO.