Title:	Non-Gaussian models for causal discovery: combining instantaneous and lagged effects
Date:	June 11, 2008 (Wednesday)
Time:	2:30 p.m. - 3:30 p.m.
Venue:	ERB712, 7/F, William M.W. Mong Engineering Building, The Chinese University of Hong Kong, Shatin, N.T.
Speaker:	Prof. Aapo Hyvarinen Senior Research Scientist Helsinki Institute for Information Technology

ABSTRACT:

Causal analysis of continuous-valued variables typically uses either autoregressive models for lagged effects or linear Bayesian networks for instantaneous effects. Estimation of Bayesian networks poses serious identifiability problems if the classic assumption of Gaussianity is made, which is why we recently proposed to use non-Gaussian models. In this talk, we first introduce our approach of linear non-Gaussian Bayesian networks (or structural equation models). Next, we propose to combine the non-Gaussian instantaneous model with autoregressive models, leading to a new variant of what is called "structural vector autoregressive" models in econometrics. We show that such a combined non-Gaussian model is identifiable without prior knowledge of network structure, and propose an estimation method shown to be consistent. This approach also points out how neglecting instantaneous effects can lead to completely wrong estimates of the autoregressive coefficients. This is joint work with Shohei Shimizu and Patrik O. Hoyer.

BIOGRAPHY:

Aapo Hyvarinen studied undergraduate mathematics and statistics at the universities of Helsinki (Finland), Vienna (Austria), and Paris (France), and finally obtained a Ph.D. degree in Information Science at the Helsinki University of Technology in 1997. After further post-doctoral work at the Helsinki University of Technology, he moved in 2003 to the Dept of Computer Science of the University of Helsinki where he worked in Academy and Senior Research Fellow positions, jointly affiliated with the Helsinki Institute for Information Technology. As of June 2008, he was appointed Professor of Computational Data Analysis, jointly at the Computer Science Dept. and the Mathematics and Statistics Dept. Dr. Hyvarinen is the first author of the book "Independent Component Analysis", and author or coauthor of more than 100 scientific articles. He is currently Action Editor at Journal of Machine Learning Research and Neural Computation, Editorial Board Member in Foundations and Trends in Machine Learning, as well as Contributing Faculty Member of Faculty of 1000 Biology. His research interests include unsupervised learning and multivariate statistics, together with their applications in neuroscience.

Enquiries: Miss Temmy So at tel 2609 8444

For more information, please refer to http://www.cse.cuhk.edu.hk/seminar