Department of Statistics
University of Florida
A Bayesian Approach for Joint Estimation of Multiple Networks
In this paper, we develop a novel Bayesian approach for joint estimation of multiple graphical models. This problem arises in many applications, such as understanding co-expression networks from high-dimensional Omics data obtained from different biological conditions or disease subtypes. We pursue a pseudo-likelihood based approach which provides robustness and computational efficiency. We illustrate the efficacy of our approach using simulated and real datasets.
This is joint work with George Michailidis and Peyman Jalali.
Friday, 2/22/2019, 11:30 AM, BLOC 113