Bani Mallick är professor i statistik vid Texas A&M. Här hittar du hans egen hemsida.

Abstract: "We propose Bayesian methods for estimating the precision matrix in Gaussian graphical models. The methods lead to sparse and adaptively shrunk estimators of the precision matrix, and thus conduct model selection and estimation simultaneously.

Our methods are based on selection priors leading to parsimonious parameterization of the precision (inverse covariance) matrix, which is essential in several applications involving learning relationships among the variables. We introduce a novel type of selection prior that develops a sparse structure of the precision matrix by making most of the elements exactly zero, in addition to ensuring positive definiteness.

We extend these methods to mixtures of Gaussian graphical models for clustered data, for which we assume each mixture component is Gaussian with an adaptive covariance structure. We discuss appropriate posterior simulation schemes to implement posterior inference in the proposed models, including the evaluation of normalizing constants that are functions of parameters of interest which result from the restrictions on the correlation matrix. Finally, we develop a network based classification model in a dual learning framework."