An efficient Bayesian approach for Gaussian Bayesian network structure learning

被引：4

作者：

Han, Shengtong

Zhang, Hongmei ^{[1
,2
]}

Homayouni, Ramin

Karmaus, Wilfried

机构：

[1] Univ Memphis, Bioinformat Program, Sch Publ Hlth, Memphis, TN 38152 USA

[2] Univ Memphis, Ctr Translat Informat, Memphis, TN 38152 USA

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2017年 / 46卷 / 07期

基金：

美国国家卫生研究院;

关键词：

DNA methylation; Gaussian DAG; MCMC; EQUIVALENCE CLASSES; GRAPHICAL MODELS; PC-ALGORITHM; INFERENCE; SAMPLER;

D O I：

10.1080/03610918.2016.1143103

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：5070 / 5084

页数：15

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