Bayesian Regressions with Tensors and Distributed Computation with Space-time Data

The Fall 2022 SDS Seminar Series continues on Friday, September 16th from 2:00 p.m. to 3:00 p.m. with Dr. Raj Guhaniyogi (Associate Professor in the Department of Statistics at Texas A&M). This event is in-person, but a virtual option will be available as well.

Title: Bayesian Regressions with Tensors and Distributed Computation with Space-time Data

Abstract: Of late, neuroscience, environmental science or related applications routinely encounter regression scenarios involving multidimensional array or tensor structured responses or predictors. In the first half of this talk, we will discuss how to perform Bayesian regression with tensor response, the construction of prior distributions on tensor-valued parameters and posterior inference. We will present applications of the proposed methodology in brain activation and brain connectome studies. The second half of this talk will be devoted to the recently emerging literature on divide-and-conquer Bayesian inference in massive spatio-temporal data. We will discuss how to draw distributed Bayesian inference in space-time data with parallel computing architecture, theoretical studies in distributed approaches and applications in large scale environmental datasets.