STA 290 Seminar: Rajarshi Guhaniyogi

STA 290 Seminar Series

Thursday, October 13th, 4:10pm, MSB 1147 (Colloquium Room)

Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)

Speaker:          Rajarshi Guhaniyogi (UC Santa Cruz)

Title: Scalable Bayesian regression framework for tensor valued objects

Abstract: This talk proposes a Bayesian approach to regression with a tensor predictor or response. Tensor covariates/responses are commonly vectorized prior to analysis, failing to exploit the structure of the tensor, and resulting in poor estimation and predictive performance. We develop a novel class of multiway shrinkage priors for the coefficients in tensor regression models. Properties are described, including posterior consistency under mild conditions, and an efficient Markov chain Monte Carlo algorithm is developed for posterior computation. Simulation studies illustrate substantial gains over vectorizing or using existing tensor regression methods in terms of estimation and parameter inference. The approach is further illustrated in a neuroimaging application.