Efficient Bayesian Regularization for Graphical Model Selection.

ABSTRACT: There has been an intense development in the Bayesian graphical model literature over the past decade; however, most of the existing methods are restricted to moderate dimensions. We propose a novel graphical model selection approach for large dimensional settings where the dimension increases with the sample size, by decoupling model fitting and covariance selection. First, a full model based on a complete graph is fit under a novel class of mixtures of inverse-Wishart priors, which induce shrinkage on the precision matrix under an equivalence with Cholesky-based regularization, while enabling conjugate updates. Subsequently, a post-fitting model selection step uses penalized joint credible regions to perform model selection. This allows our methods to be computationally feasible for larg

SUBMITTER: Kundu S 

PROVIDER: S-EPMC7592715 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature