Bayesian time series models / edited by David Barber, A. Taylan Cemgil, Silvia Chiappa.

Format:

Book

Contributor:

Barber, David, 1968- editor.

Cemgil, Ali Taylan, editor.

Chiappa, Silvia, editor.

Language:

English

Subjects (All):

Time-series analysis.

Bayesian statistical decision theory.

Physical Description:

1 online resource (xiii, 417 pages) : digital, PDF file(s).

Place of Publication:

Cambridge : Cambridge University Press, 2011.

Language Note:

English

Summary:

'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 and Silvia Chiappa

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

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

III. Switch Models: 9. Physiological monitoring with factorial switching linear dynamical systems / John A. Quinn and Christopher K.I. Williams; 10. Analysis of changepoint models / Idris A. Eckley, Paul Fearnhead and Rebecca Killick

IV. Multi-Object Models: 11. Approximate likelihood estimation of static parameters in multi-target models / Sumeetpal S. Singh, Nick Whiteley and Simon J. Godsill; 12. Sequential inference for dynamically evolving groups of objects / Sze Kim Pang, Simon J. Godsill, Jack Li, François Septier and 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 and Neil D. Lawrence; 15. Nonparametric 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

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.

Notes:

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Includes bibliographical references and index.

ISBN:

1-107-21476-9

1-139-09292-8

1-280-77593-9

1-139-09241-3

9786613686329

1-139-09101-8

1-139-09190-5

0-511-98467-7

OCLC:

782886121