The Variational Bayes Method in Signal Processing

✍ Scribed by Dr. Václav à mídl, Dr. Anthony Quinn (auth.)

• Publisher: Springer Berlin Heidelberg
• Year: 2006
• Tongue: English
• Leaves: 240
• Series: Signals and Communication Technology
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Content:
Front Matter....Pages I-XX
Introduction....Pages 1-11
Bayesian Theory....Pages 13-23
Off-line Distributional Approximations and the Variational Bayes Method....Pages 25-56
Principal Component Analysis and Matrix Decompositions....Pages 57-88
Functional Analysis of Medical Image Sequences....Pages 89-108
On-line Inference of Time-Invariant Parameters....Pages 109-144
On-line Inference of Time-Variant Parameters....Pages 145-177
The Mixture-based Extension of the AR Model (MEAR)....Pages 179-203
Concluding Remarks....Pages 205-207
Back Matter....Pages 209-227