Header menu link for other important links
Some Comments on Variational Bayes Block Sparse Modeling with Correlated Entries
S. Sharma, , Jayadeva
Published in Springer Verlag
Volume: 11455 LNCS
Pages: 110 - 117
We present some details of Bayesian block sparse modeling using hierarchical prior having deterministic and random parameters when entries within the blocks are correlated. In particular, the effect of the threshold to prune out variance parameters of algorithms corresponding to several choices of marginals, viz. multivariate Jeffery prior, multivariate Laplace distribution and multivariate Student’s t distribution, is discussed. We also provide details of experiments with Electroencephalograph (EEG) data which shed some light on the possible applicability of the proposed Sparse Variational Bayes framework. © Springer Nature Switzerland AG 2019.