Bayesian Time Series Models - David Barber

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Bayesian Time Series Models

By: David Barber (Editor), A. Taylan Cemgil (Editor), Silvia Chiappa (Editor)

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Hardcover | 11 August 2011 | Edition Number 1

At a Glance

Hardcover
432 Pages

Edition Type
New edition

Dimensions(cm)
24.41 x 16.99 x 2.39

Hardcover

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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.

Industry Reviews

'This volume is an ambitious attempt to bring researchers from many areas together into a common theme and exhibits well the challenges of such efforts in terms of finding a common ground or terminology. The book is well organized and the contributors provide highly technical material with 'brea[d]th and depth' ... The topics in the book are very broad and several of them go beyond the common theme of Bayesian time series. Perhaps an alternative title that would be more reflective of the contents of the book could be Highly Structured Probabilistic Modeling for Researchers Interested in Bayesian Methods, Modern Monte Carlo, and Time Series.' Gabriel Huerta, Journal of the American Statistical Association

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Computer Vision Machine Learning Mathematics Probability & Statistics

Contributors
Preface
Inference and estimation in probabilistic time series models
Monte Carlo:
Adaptive Markov chain Monte Carlo: theory and methods
Auxiliary particle filtering: recent developments
Monte Carlo probabilistic inference for diffusion processes: a methodological framework
Deterministic Approximations:
Two problems with variational expectation maximisation for time series models
Approximate inference for continuous-time Markov processes
Expectation propagation and generalised EP methods for inference in switching linear dynamical systems
Approximate inference in switching linear dynamical systems using Gaussian mixtures
Change-Point Models:
Analysis of change-point models
Multi-Object Models:
Approximate likelihood estimation of static parameters in multi-target models
Sequential inference for dynamically evolving groups of objects
Non-commutative harmonic analysis in multi-object tracking
Physiological monitoring with factorial switching linear dynamical systems
Non-Parametric Models:
Markov chain Monte Carlo algorithms for Gaussian processes
Non-parametric hidden Markov models
Bayesian Gaussian process models for multi-sensor time series prediction
Agent Based Models:
Optimal control theory and the linear Bellman equation
Expectation-maximisation methods for solving (PO)MDPs and optimal control problems
Index
Table of Contents provided by Publisher. All Rights Reserved.

Bayesian Time Series Models

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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