The RISC-V Reader
The Science of Programming
The art of computer programming. 4A : Part 1. Combinatorial algorithms : [the classic work extended and refined]
Theoretische Informatik
Tracking and Data Fusion
Trees, Maps, and Theorems
The Variational Bayes Method in Signal Processing
description
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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pages
N/A
Year published
2005
Publisher
Springer
Issn
978-3-540-28819-0
Language
en
id
I.5.1 SMI10