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Activity Number: 254 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #302885
Title: A Bayesian Methodology for High-Dimensional Discrete Graphical Models
Author(s): Anwesha Bhattacharyya*
Keywords: Bayesian; Ising-Potts; high-dimension; parallel computation

This work introduces a Bayesian methodology for fitting large discrete graphical models (Ising and Potts models) that uses the pseudo-likelihood instead of the likelihood. This is a powerful relaxation which allows node-wise parallel computation under separable priors. In high dimensional graphical models we can expect some sparsity in the network. The natural approach is to encode this sparsity using spike-and-slab priors. We propose the use of a weak spike and slab prior in the form of a Gaussian spike for such models. Standard MCMC algorithms can be used for the purpose of evaluating resulting posterior distribution. The use of a Bayesian approach allows us to leverage existing prior information and implement variable selection and estimation simultaneously. We illustrate the method through several examples and real data.

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

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