Synopsis
The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.
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About the Authors
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.
"About this title" may belong to another edition of this title.
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Bayesian Time Series Models
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ISBN 10: 0521196760
ISBN 13: 9780521196765
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Condition: good. This is a pre-loved book that shows moderate signs of wear from previous reading. You may notice creases, edge wear, or a cracked spine, but it remains in solid, readable condition.Please note:-May include library or rental stickers, stamps, or markings.-Supplemental materials e.g., CDs, access codes, inserts are not guaranteed.-Box sets may not come with the original outer box. If it does, the box will not be in perfect condition. -Sourced from donation centers; authenticity not verified with publisher. Your satisfaction is our top priority! If you have any questions or concerns about your order, please don't hesitate to reach out. Thank you for shopping with us and supporting small businessâ"happy reading! Seller Inventory # STM.G66
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Bayesian Time Series Models
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Bayesian Time Series Models [Hardcover] Barber, David
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Cambridge University Press, 2011
ISBN 10: 0521196760
ISBN 13: 9780521196765
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Seller: Salish Sea Books, Bellingham, WA, U.S.A.
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Condition: Very Good. Very Good Minus; Hardcover; Covers are still glossy with a few light scratches; Unblemished textblock edges; The endpapers and all text pages are clean and unmarked; The binding is excellent with a straight spine; This book will be shipped in a sturdy cardboard box with foam padding; Medium-Large Format (Quatro, 9.75" - 10.75" tall); White, purple, and orange covers with title in black lettering; 2011, Cambridge University Press; 432 pages; "Bayesian Time Series Models," by David Barber. Seller Inventory # SKU-899AD01901031
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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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First Edition
Seller: killarneybooks, Inagh, CLARE, Ireland
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Hardcover. Condition: 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. Seller Inventory # 011058
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Hardcover. Condition: 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. Seller Inventory # 9780521196765
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Bayesian Time Series Models
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