Bayesian Network--Response Regression
release_dvvn3vnleram3a5khoukula7xq
by
Lu Wang, Daniele Durante, Rex E. Jung, David B. Dunson
2016
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. The formulation leads to a general framework
for inference on changes in brain network structures across human traits,
facilitating borrowing of information and coherently characterizing
uncertainty. We provide an efficient Gibbs sampler for posterior computation
along with simple procedures for inference, prediction and goodness-of-fit
assessments. The model is applied to learn how human brain networks vary across
individuals with different intelligence scores. Results provide interesting
insights on the association between intelligence and brain connectivity, while
demonstrating good predictive performance.
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