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Book 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.

Book details

Book category Books in English Technology, engineering, agriculture Other technologies & applied sciences Applied optics

• Full title: Variational Bayes Method in Signal Processing
• Author: Václav Mídl, Anthony Quinn
• Language: English
• Binding: Paperback
• Number of pages: 228
• EAN: 9783642066900
• ISBN: 3642066909
• ID: 01651913
• Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Weight: 385 g
• Dimensions: 235 × 155 × 13 mm
• Date of publishing: 12. February 2010

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