Online Program

On Graphical Models and Convex Geometry (310023)

*Haim Bar, University of Connecticut 

Keywords: Graphical models, convex analysis, mixture models

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. (Joint work with Marty Wells).