Šmídl / Smídl / Quinn The Variational Bayes Method in Signal Processing
1. Auflage 2006
ISBN: 978-3-540-28820-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 228 Seiten, eBook
Reihe: Signals and Communication Technology
ISBN: 978-3-540-28820-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
"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."
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.
