Bayesian sparse graphical models and their mixtures.

ABSTRACT: We propose Bayesian methods for Gaussian graphical models that lead to sparse and adaptively shrunk estimators of the precision (inverse covariance) matrix. Our methods are based on lasso-type regularization priors leading to parsimonious parameterization of the precision matrix, which is essential in several applications involving learning relationships among the variables. In this context, we introduce a novel type of selection prior that develops a sparse structure on the precision matrix by making most of the elements exactly zero, in addition to ensuring positive definiteness - thus conducting model selection and estimation simultaneously. More importantly, we extend these methods to analyze clustered data using finite mixtures of Gaussian graphical model and infinite mixtures of Gaus

SUBMITTER: Talluri R 

PROVIDER: S-EPMC4059614 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature