Objective Bayes Covariate-Adjusted Sparse Graphical Model Selection

TL;DR

This paper introduces an objective Bayesian approach for covariate-adjusted sparse graphical model selection in Gaussian regression, providing a closed-form marginal likelihood that simplifies model comparison without subjective priors.

Contribution

It develops a novel objective Bayesian method with a closed-form marginal likelihood for DAG-based covariance selection, reducing subjective prior elicitation.

Findings

Provides a closed-form marginal likelihood for DAG models

Enables covariate-adjusted sparse graphical model selection

Specializes to decomposable graphical models

Abstract

We present an objective Bayes method for covariance selection in Gaussian multivariate regression models whose error term has a covariance structure which is Markov with respect to a Directed Acyclic Graph (DAG). The scope is covariate-adjusted sparse graphical model selection, a topic of growing importance especially in the area of genetical genomics (eQTL analysis). Specifically, we provide a closed-form expression for the marginal likelihood of any DAG (with small parent sets) whose computation virtually requires no subjective elicitation by the user and involves only conjugate matrix normal Wishart distributions. This is made possible by a specific form of prior assignment, whereby only one prior under the complete DAG model need be specified, based on the notion of fractional Bayes factor. All priors under the other DAG models are derived using prior modularity, and global parameter independence, in the terminology of Geiger & Heckerman (2002). Since the marginal likelihood we obtain is constant within each class of Markov equivalent DAGs, our method naturally specializes to covariate-adjusted decomposable graphical models.