The variational Bayes method in signal processing /

The variational Bayes method in signal processing /

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Bibliographic Details
Author / Creator:	Šmídl, Václav.
Imprint:	Berlin ; New York : Springer, c2006.
Description:	1 online resource (xx, 227 p.) : ill.
Language:	English
Series:	Signals and communication technology Signals and communication technology.
Subject:	Signal processing -- Statistical methods. Bayesian statistical decision theory. Bayesian statistical decision theory. Signal processing -- Statistical methods.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/8879751

Hidden Bibliographic Details
Other authors / contributors:	Quinn, Anthony (Anthony Paul)
ISBN:	9783540288206 3540288201 9786610459070 661045907X 3540288198 (Cloth) 9783540288190 (Cloth)
Notes:	Includes bibliographical references (p. [217]-224) and index. Description based on print version record.
Summary:	"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."--Jacket.
Other form:	Print version: Šmídl, Václav. Variational Bayes method in signal processing. Berlin ; New York : Springer, c2006 3540288198 9783540288190

Description
Summary:	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. Book jacket.
Physical Description:	1 online resource (xx, 227 p.) : ill.
Bibliography:	Includes bibliographical references (p. [217]-224) and index.
ISBN:	9783540288206 3540288201 9786610459070 661045907X 3540288198 9783540288190