The Variational Bayes Method in Signal Processing / Edition 1

The Variational Bayes Method in Signal Processing / Edition 1

by Vaclav #midl, Anthony Quinn
     
 

ISBN-10: 3540288198

ISBN-13: 9783540288190

Pub. Date: 12/16/2005

Publisher: Springer Berlin Heidelberg

This is the first book-length treatment of the Variational Bayes (VB) approximation in signal processing. It has been written as a self-contained, self-learning guide for academic and industrial research groups in signal processing, data analysis, machine learning, identification and control. It reviews the VB distributional approximation, showing that tractable

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Overview

This is the first book-length treatment of the Variational Bayes (VB) approximation in signal processing. It has been written as a self-contained, self-learning guide for academic and industrial research groups in signal processing, data analysis, machine learning, identification and control. It reviews the VB distributional approximation, showing that tractable algorithms for parametric model identification can be generated in off-line and on-line contexts. Many of the principles are first illustrated via easy-to-follow scalar decomposition problems. In later chapters, successful applications are found in factor analysis for medical image sequences, mixture model identification and speech reconstruction. Results with simulated and real data are presented in detail. The unique development of an eight-step "VB method", which can be followed in all cases, enables the reader to develop a VB inference algorithm from the ground up, for their own particular signal or image model.

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Product Details

ISBN-13:
9783540288190
Publisher:
Springer Berlin Heidelberg
Publication date:
12/16/2005
Series:
Signals and Communication Technology Series
Edition description:
2006
Pages:
228
Product dimensions:
9.21(w) x 6.14(h) x 0.63(d)

Table of Contents

1Introduction1
2Bayesian theory13
3Off-line distributional approximations and the variational Bayes method25
4Principal component analysis and matrix decompositions57
5Functional analysis of medical image sequences89
6On-line inference of time-invariant parameters109
7On-line inference of time-variant parameters145
8The mixture-based extension of the AR model (MEAR)179
9Concluding remarks205
Required probability distributions209

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