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Activity Number:
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254
- Novel Bayesian Methods for Structural Data: Justification and Applications
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Type:
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Invited
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Date/Time:
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Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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International Indian Statistical Association
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Abstract #320375
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Title:
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An Approximate Bayesian Approach to Covariate Dependent Graphical Modeling
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Author(s):
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Sutanoy Dasgupta and Prasenjit Ghosh and Debdeep Pati* and Bani Mallick
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Companies:
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Texas A&M University and Texas A&M University and Texas A&M University and Texas A&M University
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Keywords:
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covariate-dependent graph;
pseudo-likelihood;
risk bounds;
borrowing of information
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Abstract:
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Gaussian graphical models typically assume a homogeneous structure across all subjects, which is often restrictive in applications. In this article, we propose a weighted pseudo-likelihood approach for graphical modeling which allows different subjects to have different graphical structures depending on extraneous covariates. The pseudo-likelihood approach replaces the joint distribution by a product of the conditional distributions of each variable. We cast the conditional distribution as a heteroscedastic regression problem, with covariate-dependent variance terms, to enable information borrowing directly from the data instead of a hierarchical framework. An efficient embarrassingly parallel variational algorithm is developed to approximate the posterior and obtain estimates of the graphs. We theoretically demonstrate the advantages of information borrowing across covariate and show the practical advantages of the approach through simulation studies and illustrate the dependence structure in protein expression levels on breast cancer patients using CNV information as covariates.
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Authors who are presenting talks have a * after their name.
