Bayesian Time Series Models

David Barber, A. Taylan Cemgil, Silvia Chiappa

• Publisher: Cambridge University Press
• Year: 2011
• Print ISBN: 9780521196765
• eBook ISBN: 9781139089104
• Topic: Computer Science
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

'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.

Table of contents

• Cover
• BAYESIAN TIME SERIES MODELS
• Title
• Copyright
• Contents
• List of contributors
• Preface
• 1 Inference and estimation in probabilistic time series models
• 2 Adaptive Markov chain Monte Carlo: theory and methods
• 3 Auxiliary particle filtering: recent developments
• 4 Monte Carlo probabilistic inference for diffusion processes: a methodological framework
• 5 Two problems with variational expectation maximisation for Time Series models
• 6 Approximate inference for continuous-time Markov processes
• 7 Expectation propagation and generalised EP methods for inference in switching linear dynamical systems
• 8 Approximate inference in switching linear dynamical systems using Gaussian mixtures
• 9 Physiological monitoring with factorial switching linear dynamical systems
• 10 Analysis of changepoint models
• 11 Approximate likelihood estimation of static parameters in multi-target Models
• 12 Sequential Inference for Dynamically Evolving Groups of Objects
• 13 Non-commutative Harmonic Analysis in Multi-object Tracking
• 14 Markov chain Monte Carlo algorithms for Gaussian processes
• 15 Nonparametric hidden Markov models
• 16 Bayesian Gaussian Process Models for Multi-sensor time series prediction
• 17 Optimal control theory and the linear Bellman equation
• 18 Expectation maximisation methods for solving (PO)MDPs and optimal control problems
• Index