Numerical Analysis and Scientific Computing Seminar

Empirical Bayesian Inference using Joint Sparsity

Speaker: Anne Gelb, Dartmouth College

Location: Warren Weaver Hall (online)

Date: Oct. 30, 2020, 10 a.m.


We develop a new empirical Bayesian inference algorithm for solving a linear inverse problem given multiple measurement vectors (MMV) of under-sampled and noisy observable data.  Specifically, by exploiting the joint sparsity across the multiple measurements in the sparse domain of the underlying signal or image,  we construct a new  support informed sparsity promoting prior. Several applications can be modeled using this framework. Our numerical experiments demonstrate that using this new prior not only improves accuracy of the recovery, but also reduces the uncertainty in the posterior when compared to standard sparsity producing priors.
This is joint work with Theresa Scarnati at the Air Force Research Lab Wright Patterson and Jack Zhang, recent bachelor degree recipient at Dartmouth College and now enrolled at University of Minnesota’s PhD program in mathematics.
See for how to join the seminar via Zoom