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03317nam a22005775i 4500 |
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978-3-540-28820-6 |
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20151204142426.0 |
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100301s2006 gw | s |||| 0|eng d |
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|a 9783540288206
|9 978-3-540-28820-6
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|a 10.1007/3-540-28820-1
|2 doi
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|a 621.382
|2 23
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|a Šmídl, Václav.
|e author.
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|a The Variational Bayes Method in Signal Processing
|h [electronic resource] /
|c by Václav Šmídl, Anthony Quinn.
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| 264 |
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2006.
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| 300 |
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|a XX, 228 p. 65 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
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|a online resource
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|a text file
|b PDF
|2 rda
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|a Signals and Communication Technology
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|a 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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|a 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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|a Engineering.
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|a Application software.
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| 650 |
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|a Probabilities.
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|a Statistics.
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|a Electrical engineering.
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|a Engineering.
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| 650 |
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|a Signal, Image and Speech Processing.
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| 650 |
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|a Communications Engineering, Networks.
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| 650 |
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|a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
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| 650 |
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|a Probability Theory and Stochastic Processes.
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| 650 |
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|a Computer Applications.
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| 700 |
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|a Quinn, Anthony.
|e author.
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| 710 |
2 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9783540288190
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| 830 |
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|a Signals and Communication Technology
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/3-540-28820-1
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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