Online change-point detection in high dimensional Gaussian graphical models

Friday, September 29, 2017 - 9:30am - 11:00am
Lind 409
Hossein Keshavarz (University of Minnesota, Twin Cities)
High dimensional sparse Gaussian Graphical models are widely used for analyzing large collection of random variables interconnected by a complex dependency network, arising in many problems in biology, economics, and social sciences. Increasing availability of time evolving data sets has accentuated the need for developing models for time varying networks. Piece-wise stationary graphical models, also known as change-point models, are versatile tools for modeling time evolving high dimensional networks. The primary focus of literature is on offline detection of a single abrupt change in a large sparse network. Furthermore, the proposed algorithms are not designed to find the suddenly changed sub-graphs of the network. Therefore searching for the such sub-graphs requires extensive post-processing of the estimated parameters. In this work, we propose a scalable sequential (online) algorithm, based on pseudo-likelihood of the observations, for detecting multiple sudden changes in sub-graphs of a high dimensional sparse Gaussian graphical model.