Primarily a research monograph, this first book-length treatment of VB approximation in signal processing 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.
Published in 2005
- More infomation may be in the description section, read description carefully!
- Click "Ebook Search" button to find mirrors if no download links or dead links in the description.