Speaker: Todd Coleman (UCSD)

Title: Efficient Bayesian Inference Methods via Convex Optimization and Optimal Transport

Abstract:

In this talk, we consider many problems in Bayesian inference - from drawing samples to posteriors, to calculating confidence intervals, to implementing posterior matching algorithms, by  finding maps that push one distribution to another.  We show that for a large class of problems (with log-concave likelihoods and log-concave priors), these problems can be efficiently solved using convex optimization.  We provide example applications within the context of dynamic statistical signal processing.