CMX Lunch Seminar

Learning Sparse Non-Gaussian Graphical Models

Rebecca Morrison, Assistant Professor, Computer Science, University of Colorado Boulder,

Speaker's Bio:
Research Interests: Design of data-driven models that respect physical constraints/information Stochastic representations of model error/discrepancy/inadequacy Probabilistic graphical models and sparsity of Markov random fields Calibration, validation, and uncertainty quantification for predictive models Bayesian probability as a logical framework and causal probability

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.

Event Sponsors:

Computing and Mathematical Sciences (CMS) - More events from this Sponsor

For more information, please contact Jolene Brink by phone at 6263952813 or by email at [email protected] or visit CMX Website.