Bayesian network--response regression
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发表刊物：
Bioinformatics
摘要：
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.
合写作者：
Daniele Durante, Rex E. Jung, David B. Dunson
第一作者：
Lu Wang
卷号：
33
期号：
12
页面范围：
1859--1866
是否译文：
否
学位：博士学位
职称：副教授
学科：统计学
所在单位：数学与统计学院