The variational Bayes method in signal processing

By:

Smidl, Vaclav

Contributor(s):

Quinn,Anthony

Material type: Text

TextOriginal language: English Series: Signals and communication technologyPublication details: Berlin Springer 2011ISBN:

9783642066900(PB)

Subject(s):

DDC classification:

SMI 006.3

Contents:

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.

Summary: Synthesizes the Variational Bayes (VB) method of distributional approximation into eight clear steps ("the VB method"). When these are followed, the reader is equipped with the means to check if their model is amenable to this approximation, and to develop the approximation in a systematic way Presents some very basic toy problems involving scalar decompositions, which give insight into the nature of the method in full applications Employs the VB method in off-line and on-line scenarios in a standard and systematic way, allowing the results in each case to be compared with ease Derives all necessary results in Bayesian methods, avoiding unnecessary elaboration and making the book self-contained

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Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	IIITDM Kurnool	Non-fiction	006.3 SMI (Browse shelf(Opens below))	Available		0005327

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Synthesizes the Variational Bayes (VB) method of distributional approximation into eight clear steps ("the VB method"). When these are followed, the reader is equipped with the means to check if their model is amenable to this approximation, and to develop the approximation in a systematic way

Presents some very basic toy problems involving scalar decompositions, which give insight into the nature of the method in full applications

Employs the VB method in off-line and on-line scenarios in a standard and systematic way, allowing the results in each case to be compared with ease

Derives all necessary results in Bayesian methods, avoiding unnecessary elaboration and making the book self-contained

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