Bayesian Network–Response Regression
Lu Wang, Duke, Statistical Science
It is of increasing interest to learn how the human brain network varies as a function of continuous features, but flexible procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and Gaussian process priors to allow flexible shifts of the conditional expectation for a network-valued random variable across the values of a predictor, while including subject-specific random effects to improve prediction. We provide a simple Gibbs sampler along with procedures for inference, prediction, and goodness-of-fit assessments. The model is applied to learn changes in the brain network across intelligence scores.
March, 7 2016 | 12:30 p.m. - 2:00 p.m. | 270 Gross Hall