The Variational Bayes Method in Signal Processing

  • Václav Šmídl
  • Anthony Quinn

Part of the Signals and Communication Technology book series (SCT)

About this book

Introduction

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.

Keywords

Bayesian Theory Factor analysis On-line Inference Signal algorithm calculus data analysis learning machine learning model principal component analysis signal processing

Authors and affiliations

  • Václav Šmídl
    • 1
  • Anthony Quinn
    • 2
  1. 1.Department of Adaptive SystemsInstitute of Information Theory and Automation, Academy of Sciences of the Czech RepublicPraha 8Czech Republic
  2. 2.Department of Electronic and Electrical EngineeringUniversity of Dublin, Trinity CollegeDublin 2Ireland

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-28820-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-28819-0
  • Online ISBN 978-3-540-28820-6
  • About this book