Professor Jingchen Liu

Columbia University

Title: A Fused Latent and Graphical Model

Abstract: One of the main tasks of statistical models is to characterize the dependence structures of multi-dimensional distributions. Latent variable model takes advantage of the fact that the dependence of a high dimensional random vector is often induced by just a few latent (unobserved) factors. In this talk, we present several problems regarding latent variable models. When the dimension grows higher and the dependence structure becomes more complicated, it is hardly possible to find a low dimensional parametric latent variable model that fits well. We enrich the model by including a graphical structure on top of the latent structure. Thus, the main variation of the random vector remains governed by the latent variables. The graph captures the remaining dependence. I will also present some examples in which both the latent variable and the conditional graph have practical interpretations.

Bio: Jingchen Liu is Associate Professor in the Department of Statistics at Columbia University. He holds a Ph.D. in Statistics from Harvard University. He is the recipient of 2013 Tweedie New Researcher Award given by the Institute of Mathematical Statistics and a recipient of the 2009 Best Publication in Applied Probability Award given by the INFORMS Applied Probability Society. He has research interests in statistics, applied probability, Monte Carlo methods, and psychometrics.