Han, Shengtong; Zhang, Hongmei; Homayouni, Ramin; Karmaus, Wilfried An efficient Bayesian approach for Gaussian Bayesian network structure learning. (English) Zbl 1377.62142 Commun. Stat., Simulation Comput. 46, No. 7, 5070-5084 (2017). Summary: 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. MSC: 62H12 Estimation in multivariate analysis 62P10 Applications of statistics to biology and medical sciences; meta analysis 05C90 Applications of graph theory Keywords:Gaussian DAG; MCMC; Bayesian network; structure learning; directed acyclic graphs (DAGs) PDFBibTeX XMLCite \textit{S. Han} et al., Commun. Stat., Simulation Comput. 46, No. 7, 5070--5084 (2017; Zbl 1377.62142) Full Text: DOI Link