Bayesian network--response regression

Release time:2018-09-07

Journal:Bioinformatics

Abstract:There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and flexible Gaussian process priors to learn changes in the conditional expectation of a network-valued random variable across the values of a continuous predictor, while including subject-specific random effects.

Co-author:Daniele Durante, Rex E. Jung, David B. Dunson

First Author:Lu Wang

Volume:33

Issue:12

Page Number:1859--1866

Translation or Not:no

Date of Publication:2017-07-01