An Efficient Bayesian Approach for Gaussian Bayesian Network Structure Learning

Overview

Journal Commun Stat Simul Comput

Date 2019 Mar 29

PMID 30918419

Citations 3

Authors

Shengtong Han

Hongmei Zhang

Ramin Homayouni

Wilfried Karmaus

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

The role of epigenetics in multi-generational transmission of asthma: An NIAID workshop report-based narrative review.

Wheatley L, Holloway J, Svanes C, Sears M, Breton C, Fedulov A Clin Exp Allergy. 2022; 52(11):1264-1275.

PMID: 36073598 PMC: 9613603. DOI: 10.1111/cea.14223.

Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo.

Yu K, Cui Z, Sui X, Qiu X, Zhang J Front Genet. 2021; 12:764020.

PMID: 34912373 PMC: 8668238. DOI: 10.3389/fgene.2021.764020.

Gaussian Bayesian network comparisons with graph ordering unknown.

Zhang H, Huang X, Han S, Rezwan F, Karmaus W, Arshad H Comput Stat Data Anal. 2021; 157.

PMID: 33408431 PMC: 7781289. DOI: 10.1016/j.csda.2020.107156.

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