A0995
Title: A Bayesian subset specific approach to joint selection of multiple graphical models
Authors: Kshitij Khare - University of Florida (United States) [presenting]
Peyman Jalali - Wells Fargo (United States)
George Michailidis - University of California, Los Angeles (United States)
Abstract: The problem of joint estimation of multiple graphical models from high dimensional data has been studied in statistics and machine learning, due to its importance in diverse fields including molecular biology, neuroscience and the social sciences. A Bayesian approach is developed which decomposes the model parameters across the multiple graphical models into shared components across subsets of models and edges, and idiosyncratic ones. Further, it leverages a novel multivariate prior distribution, coupled with a jointly convex regression-based pseudo-likelihood that enables fast computations through a robust and efficient Gibbs sampling scheme. We establish strong posterior consistency for model selection under high dimensional scaling, with the number of variables growing exponentially as a function of the sample size.
