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Abstract:
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In this talk, I will discuss new methods for the simultaneous inference of graphical models and covariates effects in the Bayesian framework. I will consider settings where we are interested in estimation of sparse networks among a set of primary variables, where covariates may impact either the node abundances or the strength of edges. Our 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. I will illustrate these methods through both simulation studies and application to high-dimensional microbiome or neuroimaging data sets.
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