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Bayesian time series models / edited by David Barber, A. Taylan Cemgil, Silvia Chiappa
Mitwirkende(r): Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Cambridge : Cambridge University Press, 2011Beschreibung: 1 Online-Ressource (xiii, 417 pages) : digital, PDF file(s)ISBN:- 9780511984679
- 519.5/5 22
- QA280
Inhalte:
Zusammenfassung: '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 practicePPN: PPN: 883390205Package identifier: Produktsigel: ZDB-20-CTM | ZDB-20-CBO
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
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
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
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
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
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
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