November 13, 2012 @ 3:30 p.m. - 4:30 p.m.
Han Liu, Ph.D., Princeton University
Location: 701 Blockley Hall
Abstract: The graphical model has proven to be a useful abstraction in statistics and machine learning. The starting point is the graph of a distribution. While often the graph is assumed given, we are interested in estimating the graph from data. In this talk we present new nonparametric and semiparametric methods for graph estimation. The performance of these methods is illustrated and compared on several real and simulated examples.