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David Barber; A Taylan Cemgil; Silvia ChiappaBiblio.com

Bayesian Time Series Models

2011Best.-Nr. 11058Cambridge University Press 2011. 1st Edition . Hardcover. Very Good. Oversized hardcover xiii 417pp 4 pages of plates shipping weight over 1kg NOT ex-library. Owner's name inside the front board covered with a blank sticker. Book is clean and bright with unmarked text free of stamps firmly bound. Issued without a dust jacket. -- '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. -- Contents: 1. Inference and estimation in probabilistic time series models / David Barber A. Taylan Cemgil & Silvia Chiappa University of Cambridge; -- I. Monte Carlo -- 2. Adaptive Markov chain Monte Carlo: theory and methods / Yves Atchad Gersende Fort Eric Moulines & Pierre Priouret; 3. Auxiliary particle filtering: recent developments / Nick Whiteley & 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 & Maneesh Sahani; 6. Approximate inference for continuous-time Markov processes / C dric Archambeau & Manfred Opper; 7. Expectation propagation and generalised EP methods for inference in switching linear dynamical systems / Onno Zoeter & Tom Heskes; 8. Approximate inference in switching linear dynamical systems using Gaussian mixtures / David Barber; -- III. Switching models -- 9. Physiological monitoring with factorial switching linear dynamical systems / John A. Quinn & Christopher K.I. Williams; 10. Analysis of changepoint models / Idris A. Eckley Paul Fearnhead & Rebecca Killick; -- IV. Multi-object models -- 11. Approximate likelihood estimation of static parameters in multi-target models / Sumeetpal S. Singh Nick Whiteley & Simon J. Godsill; 12. Sequential inference for dynamically evolving groups of objects / Sze Kim Pang Simon J. Godsill Jack Li Fran ois Septier & 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 & Neil D. Lawrence; 15. Nonparametric hidden Markov models / Jurgen Van Gael & Zoubin Ghahramani; 16. Bayesian Gaussian process models for multi-sensor time series prediction / Michael A. Osborne Alex Rogers Stephen J. Roberts Sarvapali D. Ramchurn & 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 POMDPs and optimal control problems / Marc Toussaint Amos Storkey & Stefan Harmeling Biological Cybernetics; Index Cambridge University Press hardcover

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