Variational Bayes Method in Signal Processing

Name: Variational Bayes Method in Signal Processing
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783540288190
Price: 2567 CZK
Availability: InStock

Autor: Anthony Quinn, Vaclav Smidl

Jazyk:

Vazba: Pevná

Vydavatel: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Dostupnost: Skladem u dodavatele v malém množství

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2 567 Kč

This first book-length treatment of the Variational Bayes (VB) approximation in signal processing. I...

Informace o knize

Autor

Anthony Quinn, Vaclav Smidl

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2005

Stránek

228

EAN

9783540288190

ISBN

3540288198

Enbook ID

01561123

Vydavatel

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Hmotnost

1160

Rozměry

155 x 235 x 22

Kompletní popis

This 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 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. TOC:Introduction.- Bayesian Theory..- Off-line Distributional Approximations and the Variational Bayes Method.- Principal Component Analysis and Matrix Decompositions.- Functional Analysis of Medical Image Sequences.- On-line Inference of Time-Invariant Parameters.- On-line Inference of Time-Variant Parameters.- The Mixture-based Extension of the AR Model (MEAR).- Concluding Remarks.

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Variational Bayes Method in Signal Processing

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