|Thursday, February 23|
|PS1 Poster Session 1 and Opening Mixer||
Thu, Feb 23, 5:30 PM - 7:00 PM
Conference Center AB
More Than Meets the Eye: Bayesian Inference in Nonparanormal Graphical Models (303425)Subhashis Ghosal, North Carolina State University
*Jami Jackson Mulgrave, North Carolina State University
Keywords: graphical models, Bayesian, nonparanormal
Graphical models are mathematical models that are used to describe relationships in data. In addition to being visually appealing, graphical models are functional and can be used to discover and learn structure in the data due to the probabilistic relationships between variables that are encoded in the graph. Graphical models have been used in computer vision, speech recognition, and modeling of biological pathways. In this poster, current research on Bayesian inference in nonparanormal graphical models will be introduced. Real data applications and packages that can be used to implement these models will be discussed in detail.