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Activity Number: 563 - Recent Advances in Bayesian Structure Learning
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #304124 Presentation
Title: Bayesian Structure Learning in Graphical Models Using Shrinkage Priors
Author(s): Sayantan Banerjee*
Companies: Indian Institute of Management Indore
Keywords: Bayesian structure learning; Graphical models; Posterior convergence; Precision matrix

We consider the problem of structure learning in graphical models corresponding to $p$-variate Gaussian random variables, where the dimension $p$ might be large. The conditional independence structure of the random variables can be elicited from the zero structure of the corresponding precision (or, inverse covariance) matrix, and Gaussian graphical models act as important tools to explore the same in this context. We follow a Bayesian approach for graphical model structure learning using shrinkage priors on the precision matrix. We develop an efficient MCMC algorithm for sampling from the posterior distribution and also provide theoretical guarantees for posterior convergence under the $L_2$-norm.

Authors who are presenting talks have a * after their name.

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