The speaker will present a Bayesian model selection (BMS) approach to detect abnormalities in the data from magnetic resonance imaging guided radiation therapy devices. The BMS method effectively identifies the true abnormalities and suppress the spurious ones. The speaker will discuss several extensions, including detecting structural changes in heat-maps and extracting dynamic resting-state functional connectivity from brain images. The speaker will also introduce a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The method was used to analyze the functional relationship between ambulatory blood pressure trajectories and clinical outcome in stroke patients
Contact kkanagaw@stanford.edu for required meeting password.
