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Activity Number: 665
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #320853
Title: Bayesian Method for Testing Differential Directed Acyclic Graphs
Author(s): Hongmei Zhang* and Xianzheng Huang and Shengtong Han and Wilfried Karmaus
Companies: University of Memphis and University of South Carolina and The University of Chicago and University of Memphis
Keywords: Bayesian network ; Variable selection ; Differential networks

Graphical models are essential to describe networks among different factors. Various methods to construct graphs have been proposed. In practice, it is critical to assess the agreement between networks constructed under different treats, e.g., a network formed by a large number of epigenetic factors (e.g., DNA methylation) among subjects who smoke versus that among non-smokers. There is rather limited effort in this area. We propose a Bayesian method to build directed acyclic graphs (DAGs) based on a given order of nodes and simultaneously test the agreement between DAGs. The network construction and differential graphs testing are built upon the concept of variable selection. Simulations demonstrate the applicability of the method and a real data application to an epigenetic data is implemented to illustrate the method.

Authors who are presenting talks have a * after their name.

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