B1386
Title: An approximate Bayesian approach to covariate dependent graphical modeling
Authors: Sutanoy Dasgupta - Texas A and M University (United States)
Debdeep Pati - Texas A&M University (United States) [presenting]
Bani Mallick - Texas A&M University (United States)
Prasenjit Ghosh - Texas A and M University (United States)
Abstract: Gaussian graphical models typically assume a homogeneous structure across all subjects, which is often restrictive in applications. We propose a weighted pseudo-likelihood approach for graphical modeling which allows different subjects to have different graphical structures depending on extraneous covariates. The pseudo-likelihood approach replaces the joint distribution by a product of the conditional distributions of each variable. We cast the conditional distribution as a heteroscedastic regression problem, with covariate-dependent variance terms, to enable information borrowing directly from the data instead of a hierarchical framework. This allows independent graphical modelling for each subject, while retaining the benefits of a hierarchical Bayes model and being computationally tractable. An efficient embarrassingly parallel variational algorithm is developed to approximate the posterior and obtain estimates of the graphs. Using a fractional variational framework, we derive asymptotic risk bounds for the estimate in terms of a novel variant of the alpha-Renyi divergence. We theoretically demonstrate the advantages of information borrowing across covariates over independent modelling across covariates. We show the practical advantages of the approach through simulation studies and illustrate the dependence structure in protein expression levels on breast cancer patients using CNV information as covariates.
