Causal Inference in Stochastic Networks: Going Beyond Linear Models
Speaker: Negar Kiyavash, University of Illinois-Urbana-Champaign
Department: Electrical Engineering
Location: Engineering Quadrangle B205
Date/Time: Thursday, September 26, 2013, 3:30 p.m. - 4:30 p.m.
Directed information graphs are a new type of probabilistic graphical model based on directed information that represent the casual dynamics among random processes in a stochastic systems. In this talk, we present a framework for learning the structure of such graphs which goes beyond linear models studied in the literature. Additionally, in the presence of large data, we propose algorithms that identify optimal or near optimal approximations to the topology.