Title: On Graphical Models and Convex Geometry

Abstract: We introduce a mixture-model of beta distributions to identify significant correlations among P predictors when P is large. The method relies on theorems in convex geometry, which we use to show how to control the error rate of edge detection in graphical models. Our ‘betaMix’ method does not require any assumptions about the network structure, nor does it assume that the network is sparse. The results hold for a wide class of data generating distributions that include light-tailed and heavy-tailed spherically symmetric distributions.

Bio: Haim Bar is an Associate Professor in the Department of Statistics, University of Connecticut. He received his Ph.D. in statistics at Cornell University, an M.Sc. in computer science at Yale University, and a B.Sc. in mathematics at the Hebrew University in Jerusalem. His professional interests include statistical modeling, shrinkage estimation, high throughput applications in biology (e.g., genomics), Bayesian statistics, variable selection, and machine learning. Before joining UConn, he worked as a software engineer for Motorola, a director of software development in MicroPatent, LLC, a Principal Scientist at ATC-NY, and a statistician at Cornell's Statistical Consulting Unit.