This article proposes a Bayesian computing algorithm to infer Gaussian directed acyclic graphs (DAG's). 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 DAG's. We applied the algorithm to an epigenetic data set to infer DAG's for smokers and non-smokers.
Keywords: DNA methylation; Gaussian DAG; MCMC.