Bayesian time series models

著者

書誌事項

Bayesian time series models

edited by David Barber, A. Taylan Cemgil, Silvia Chiappa

Cambridge University Press, 2011

  • : hardback

大学図書館所蔵 件 / 全18件

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

'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.

目次

  • 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.

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