Bayesian time series models / edited by David Barber, A. Taylan Cemgil, Silvia Chiappa
| データ種別 | 電子ブック |
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| 出版者 | (Cambridge : Cambridge University Press) |
| 出版年 | 2011 |
| 大きさ | 1 online resource (xiii, 417 pages) : digital, PDF file(s) |
目次／あらすじ
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E-Book | 08701901 | 同時アクセス無制限 | 9780511984679 |
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| 内容注記 | Inference and estimation in probabilistic time series models / David Barber, A. Taylan Cemgil and Silvia Chiappa Monte Carlo / Yves Atchadé, Gersende Fort, Eric Moulines and Pierre Priouret Adaptive Markov chain Monte Carlo: theory and methods / Nick Whiteley and Adam M. Johansen Auxiliary particle filtering: recent developments / Omiros Papaspiliopoulos Monte Carlo probabilistic inference for diffusion processes: a methodological framework / Richard Eric Turner and Maneesh Sahani Deterministic Approximations / Cédric Archambeau and Manfred Opper Two problems with variational expectation maximisation for time series models / Onno Zoeter and Tom Heskes Approximate inference for continuous-time Markov processes / David Barber Expectation propagation and generalised EP methods for inference in switching linear dynamical systems / John A. Quinn and Christopher K.I. Williams Approximate inference in switching linear dynamical systems using Gaussian mixtures / Idris A. Eckley, Paul Fearnhead and Rebecca Killick Switch Models / Sumeetpal S. Singh, Nick Whiteley and Simon J. Godsill Physiological monitoring with factorial switching linear dynamical systems / Sze Kim Pang, Simon J. Godsill, Jack Li, François Septier and Simon Hill Analysis of changepoint models / Risi Kondor Multi-Object Models / Michalis K. Titsias, Magnus Rattray and Neil D. Lawrence Approximate likelihood estimation of static parameters in multi-target models / Jurgen Van Gael and Zoubin Ghahramani Sequential inference for dynamically evolving groups of objects / Michael A. Osborne, Alex Rogers, Stephen J. Roberts, Sarvapali D. Ramchurn and Nick R. Jennings Non-commutative harmonic analysis in multi-object tracking / Hilbert J. Kappen Nonparametric Models / Marc Toussaint, Amos Storkey and Stefan Harmeling Nonparametric hidden Markov models Bayesian Gaussian process models for multi-sensor time series prediction Agent-Based Models Expectation maximisation methods for solving (PO)MDPs and optimal control problems |
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| 一般注記 | Title from publisher's bibliographic system (viewed on 05 Oct 2015) '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 HTTP:URL=https://doi.org/10.1017/CBO9780511984679 |
| 著者標目 | Barber, David 1968- editor Cemgil, Ali Taylan editor Chiappa, Silvia editor |
| 件 名 | LCSH:Time-series analysis LCSH:Bayesian statistical decision theory |
| 分 類 | LCC:QA280 DC22:519.5/5 |
| 書誌ID | 8000277632 |
| ISBN | 9780511984679 |