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Bayesian Time Series Models

David Barber, A. Taylan Cemgil, and Silvia Chiappa, editors
Publisher: 
Cambridge University Press
Publication Date: 
2011
Number of Pages: 
417
Format: 
Hardcover
Price: 
80.00
ISBN: 
9780521196765
Category: 
Anthology
We do not plan to review this book.

Contributors
Preface
1. Inference and estimation in probabilistic time series models David Barber, A. Taylan Cemgil and Silvia Chiappa
Part 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
Part 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
Part III. Change-Point Models: 9. Analysis of change-point models Idris A. Eckley, Paul Fearnhead and Rebecca Killick
Part IV. Multi-Object Models: 10. Approximate likelihood estimation of static parameters in multi-target models Sumeetpal S. Singh, Nick Whiteley and Simon J. Godsill
11. Sequential inference for dynamically evolving groups of objects Sze Kim Pang, Simon J. Godsill, Jack Li, François Septier and Simon Hill
12. Non-commutative harmonic analysis in multi-object tracking Risi Kondor
13. Physiological monitoring with factorial switching linear dynamical systems John A. Quinn and Christopher K. I. Williams
Part V. Non-Parametric Models: 14. Markov chain Monte Carlo algorithms for Gaussian processes Michalis K. Titsias, Magnus Rattray and Neil D. Lawrence
15. Non-parametric 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
Part 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
Index.

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