Bayesian Time Series Models

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David Barber, A. Taylan Cemgil, Silvia Chiappa
Cambridge University Press, Aug 11, 2011 - Computers - 417 pages
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'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.
 

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Contents

1 Inference and estimation in probabilistic time series models David Barber A Taylan Cemgil and Silvia Chiappa
1
theory and methods Yves Atchadé Gersende Fort Eric Moulines and Pierre Priouret
32
recent developments Nick Whiteley and Adam M Johansen
52
a methodological framework Omiros Papaspiliopoulos
82
5 Two problems with variational expectation maximisation for Time Series models Richard Eric Turner and Maneesh Sahani
104
6 Approximate inference for continuoustime Markov processes Cédric Archambeau and Manfred Opper
125
7 Expectation propagation and generalised EP methods for inference in switching linear dynamical systems Onno Zoeter and Tom Heskes
141
8 Approximate inference in switching linear dynamical systems using Gaussian mixtures David Barber
166
11 Approximate likelihood estimation of static parameters in multitarget Models Sumeetpal S Singh Nick Whiteley and Simon J Godsill
225
12 Sequential Inference for Dynamically Evolving Groups of Objects Sze Kim Pang Simon J Godsill Jack Li François Septier and Simon Hill
245
13 Noncommutative Harmonic Analysis in Multiobject Tracking Risi Kondor
277
14 Markov chain Monte Carlo algorithms for Gaussian processes Michalis K Titsias Magnus Rattray and Neil D Lawrence
295
15 Nonparametric hidden Markov models Jurgen Van Gael and Zoubin Ghahramani
317
16 Bayesian Gaussian Process Models for Multisensor time series prediction Michael A Osborne Alex Rogers Stephen J Roberts Sarvapali D Ramchu...
341
17 Optimal control theory and the linear Bellman equation Hilbert J Kappen
363
18 Expectation maximisation methods for solving POMDPs and optimal control problems Marc Toussaint Amos Storkey and Stefan Harmeling
388

9 Physiological monitoring with factorial switching linear dynamical systems John A Quinn and Christopher K I Williams
182
10 Analysis of changepoint models Idris A Eckley Paul Fearnhead and Rebecca Killick
205

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About the author (2011)

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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