An efficient Bayesian approach for Gaussian Bayesian network structure learning

This article proposes a Bayesian computing algorithm to infer Gaussian directed acyclic graphs (DAGs). It has the ability of escaping local modes and maintaining adequate computing speed compared to existing methods. Simulations demonstrated that the proposed algorithm has low false positives and false negatives in comparison to an algorithm applied to DAGs. We applied the algorithm to an epigenetic dataset to infer DAG's for smokers and nonsmokers.