The Variational Bayes Method In Signal Processing
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The Variational Bayes Method in Signal Processing
Author: Václav Šmídl
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-03-30
Treating VB approximation in signal processing, this monograph is for academic and industrial research groups in signal processing, data analysis, machine learning and identification. It reviews distributional approximation, showing that tractable algorithms for parametric model identification can be generated in off-line and on-line contexts.
The Variational Bayes Method in Signal Processing
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.
Variational Bayesian Learning Theory
Author: Shinichi Nakajima
language: en
Publisher: Cambridge University Press
Release Date: 2019-07-11
This introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.