Inhaltsangabe
The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.
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Über die Autorinnen und Autoren
David Barber is a Reader in Information Processing at University College London.
A. Taylan Cemgil is an Assistant Professor in the Department of Computer Engineering at Boğaziçi University, Istanbul.
Silvia Chiappa is a Marie Curie Fellow at the Statistical Laboratory, Cambridge.
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Bayesian Time Series Models
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Cambridge University Press, 2011
ISBN 10: 0521196760
ISBN 13: 9780521196765
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Cambridge University Press, 2011
ISBN 10: 0521196760
ISBN 13: 9780521196765
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Hardcover. Zustand: Very Good. 1st Edition. 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 Atchadé, 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 / Cédric 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, François 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 (PO)MDPs and optimal control problems / Marc Toussaint, Amos Storkey & Stefan Harmeling, Biological Cybernetics; Index. Bestandsnummer des Verkäufers 011058
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Bayesian Time Series Models [Hardcover] Barber, David
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ISBN 13: 9780521196765
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Hardcover. Zustand: new. Hardcover. '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. 'What's going to happen next?' Time series data hold the answers. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Readers with only a basic understanding of applied probability are guided from fundamental concepts to the state-of-the-art in research and practice. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9780521196765
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