Bayesian time series models /
edited by David Barber, A. Taylan Cemgil, Silvia Chiappa.
- Cambridge : Cambridge University Press, 2011.
- 1 online resource (xiii, 417 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Inference and estimation in probabilistic time series models / Monte Carlo: Adaptive Markov chain Monte Carlo: theory and methods / Auxiliary particle filtering: recent developments / Monte Carlo probabilistic inference for diffusion processes: a methodological framework / Deterministic Approximations: Two problems with variational expectation maximisation for time series models / Approximate inference for continuous-time Markov processes / Expectation propagation and generalised EP methods for inference in switching linear dynamical systems / Approximate inference in switching linear dynamical systems using Gaussian mixtures / Switch Models: Physiological monitoring with factorial switching linear dynamical systems / Analysis of changepoint models / Multi-Object Models: Approximate likelihood estimation of static parameters in multi-target models / Sequential inference for dynamically evolving groups of objects / Non-commutative harmonic analysis in multi-object tracking / Nonparametric Models: Nonparametric hidden Markov models / Bayesian Gaussian process models for multi-sensor time series prediction / Agent-Based Models: Expectation maximisation methods for solving (PO)MDPs and optimal control problems / David Barber, A. Taylan Cemgil and Silvia Chiappa -- Yves Atchadé, Gersende Fort, Eric Moulines and Pierre Priouret; Nick Whiteley and Adam M. Johansen; Omiros Papaspiliopoulos -- Richard Eric Turner and Maneesh Sahani; Cédric Archambeau and Manfred Opper; Onno Zoeter and Tom Heskes; David Barber -- John A. Quinn and Christopher K.I. Williams; Idris A. Eckley, Paul Fearnhead and Rebecca Killick -- Sumeetpal S. Singh, Nick Whiteley and Simon J. Godsill; Sze Kim Pang, Simon J. Godsill, Jack Li, François Septier and Simon Hill; Risi Kondor -- Michalis K. Titsias, Magnus Rattray and Neil D. Lawrence; Jurgen Van Gael and Zoubin Ghahramani; Michael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn and Nick R. Jennings -- Hilbert J. Kappen; Marc Toussaint, Amos Storkey and Stefan Harmeling. 1. I. 2. 3. 4. II. 5. 6. 7. 8. III. 9. 10. IV. 11. 12. 13. V. 14. Markov chain Monte Carlo algorithms for Gaussian processes / 15. 16. VI. 17. Optimal control theory and the linear Bellman equation / 18.
'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.