Bayesian Time Series Models

'This volume is an ambitious attempt to bring researchers from many areas together into a common theme and exhibits well the challenges of such efforts in terms of finding a common ground or terminology. The book is well organized and the contributors provide highly technical material with 'brea[d]th and depth' ... The topics in the book are very broad and several of them go beyond the common theme of Bayesian time series. Perhaps an alternative title that would be more reflective of the contents of the book could be Highly Structured Probabilistic Modeling for Researchers Interested in Bayesian Methods, Modern Monte Carlo, and Time Series.' Gabriel Huerta, Journal of the American Statistical Association

David Barber is a Reader in Information Processing at University College London. A. Taylan Cemgil is an Assistant Professor in the Department of Computer Engineering at Boazii University, Istanbul. Silvia Chiappa is a Marie Curie Fellow at the Statistical Laboratory, Cambridge.

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 Atchad, 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 Cdric 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, Franois 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.