000			01683nam a22002177a 4500
005			20240216152637.0
008			240216b |||||||| |||| 00| 0 eng d
020			_a9783642066900(PB)
041			_heng
082			_bSMI _a006.3
100			_aSmidl, Vaclav
245			_a The variational Bayes method in signal processing
260			_aBerlin _bSpringer _c2011
440			_a Signals and communication technology
505			_aBayesian 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.
520			_aSynthesizes 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
650			_aBayesian statistical decision theory
650			_aSignal processing Statistical methods
700			_aQuinn,Anthony
942			_2ddc _cBK
999			_c2076 _d2076