Bayesian Time Series Models (Hardcover)

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 Atchade, 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 Cedric 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, Francois 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.