CMX Lunch Seminar

Wednesday December 9, 2020 12:00 PM

Learning Sparse Non-Gaussian Graphical Models

Speaker: Rebecca Morrison, Computer Science, University of Colorado Boulder
Location: Online Event

Identification and exploitation of a sparse undirected graphical model (UGM) can simplify inference and prediction processes, illuminate previously unknown variable relationships, and even decouple multi-domain computational models. In the continuous realm, the UGM corresponding to a Gaussian data set is equivalent to the non-zero entries of the inverse covariance matrix. However, this correspondence no longer holds when the data is non-Gaussian. In this talk, we explore a recently developed algorithm called SING (Sparsity Identification of Non-Gaussian distributions), which identifies edges using Hessian information of the log density. Various data sets are examined, with sometimes surprising results about the nature of non-Gaussianity.

Series CMX Lunch Series

Contact: Jolene Brink at 6263952813 jbrink@caltech.edu
For more information visit: http://cmx.caltech.edu/