The Variational Bayes Method in Signal Processing [electronic resource] /by Václav Šmídl, Anthony Quinn.
by Šmídl, Václav [author.]; Quinn, Anthony [author.]; SpringerLink (Online service).
Material type:
BookSeries: Signals and Communication Technology: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.Description: XX, 227 p. 65 illus. online resource.ISBN: 9783540288206.Subject(s): Engineering | Computer science | Distribution (Probability theory) | Statistics | Engineering | Signal, Image and Speech Processing | Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences | Probability Theory and Stochastic Processes | Computer ApplicationsDDC classification: 621.382 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| TA1637-1638 (Browse shelf) | Available | ||||
| TK7882.S65 (Browse shelf) | Available | ||||
| Long Loan | MAIN LIBRARY | TK5102.9 (Browse shelf) | Available |
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| TK7882.S65 Speech Enhancement | TK7882.S65 Digital Image Processing | TK7882.S65 Image Processing Using Pulse-Coupled Neural Networks | TK7882.S65 The Variational Bayes Method in Signal Processing | TK7882.S65 Detection and Signal Processing | TK7882.S65 Remote Sensing Digital Image Analysis | TK7882.S65 Topics in Acoustic Echo and Noise Control |
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.
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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