A0299
Title: A fused latent and graphical model
Authors: Jingchen Liu - Columbia University (United States) [presenting]
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. Such models are employed in the analysis of marketing, e-commerce, social network, and many other fields where human behaviors are observed and are summarized to a few characteristics. We present several examples. In these examples, a common problem is that 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. The graph captures the remaining dependence and is often more interpretable than graphs built on marginal dependence.
