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Title: Scalable Bayesian models for inference of networks and covariate effects Authors:  Marina Vannucci - Rice University (United States) [presenting]
Abstract: New methods for the simultaneous inference of graphical models and covariates effects in the Bayesian framework will be discussed. We will consider settings where we are interested in the estimation of sparse networks among a set of primary variables, where covariates may impact the strength of edges. The proposed model utilizes spike-and-slab priors to perform edge selection, and Gaussian process priors to allow for flexibility in the covariate effects. In order to estimate these models, we rely on efficient deterministic algorithms based on variational inference. Simulation studies demonstrate how the proposed model improves on the accuracy of previous models in both network recovery and covariate selection. We apply the proposed model to fMRI data, learning both a functional network between brain regions and how the strength of network edges varies based on subject-level covariates, such as age and gender.