The variational Bayes method in signal processing
Smidl, Vaclav
The variational Bayes method in signal processing - Berlin Springer 2011 - Signals and communication technology .
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.
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
9783642066900(PB)
Bayesian statistical decision theory
Signal processing Statistical methods
006.3 / SMI
The variational Bayes method in signal processing - Berlin Springer 2011 - Signals and communication technology .
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.
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
9783642066900(PB)
Bayesian statistical decision theory
Signal processing Statistical methods
006.3 / SMI