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Activity Number: 505
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: International Chinese Statistical Association
Abstract #320794 View Presentation
Title: A Gaussian-Probit Model for Bayesian Network with Mixed Variables
Author(s): Qingyang Zhang* and Ji-Ping Wang
Companies: University of Arkansas and Northwestern University
Keywords: Bayesian network ; Causal inference ; Mixed variables ; Penalized likelihood ; Probit model ; Ovarian Cancer
Abstract:

We consider fitting a Bayesian network model to the data where the nodes are mixed with continuous and discrete variables. Most existing approaches either assume that all nodes follow a Gaussian distribution or all nodes follow a multinomial distribution, which limits their applicability to many real-world problems. In this paper, we propose a Gaussian-Probit Bayesian network model with a penalized maximum likelihood estimation using a blockwise coordinate descent (BCD) algorithm. We demonstrate that causal relationships among nodes in a sparse network can be effectively predicted, and the prediction accuracy can be further improved using a consensus network obtained from multiple predictions. The proposed methods were applied to The Cancer Genome Atlas data to study the (epi)genetic pathways that underlie ovarian cancer progression.


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

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