2019 Mar 15, Jyotishka Datta, University of Arkansas New directions in Bayesian sparse signal recovery

Jyotishka Datta, University of Arkansas
New directions in Bayesian sparse signal recovery  
Sparse signal recovery remains an important challenge in large scale data analysis and global-local (G-L) shrinkage priors have emerged as the current state-of-the art Bayesian method handling sparsity. In the first half of this talk, I will survey some of the recent theoretical and methodological advances in this area, focusing on theoretical optimality of G-L priors in the context of multiple testing for both continuous as well as quasi-sparse count data. In the second half, I will discuss a few unexplored aspects of their behavior, such as, validity as a non-convex regularization method, performance in presence of correlated errors or extension to discrete data structures including sparse compositional data. I will offer some insights into some of these problems and point out future directions.