on Fridays starting at 10:15 a.m. Coffee available from 10.
Fri Dec 14
Patrik Hoyer
Exploiting both non-gaussianity and conditional independencies for causal discovery
Abstract:
I will describe my recent visit to Carnegie Mellon University in Pittsburgh, both in terms of the general experience and in terms of the scientific results. In particular, I will describe techniques for learning linear causal models that combine the strengths of their methods based on conditional independencies and our algorithms based on non-gaussianity.
This is joint work with Peter Spirtes, Richard Scheines, Clark Glymour, Joseph Ramsey and Gustavo Lacerda at Carnegie Mellon University, Shohei Shimizu at the Institute of Statistical Mathematics in Tokyo, and Aapo Hyvärinen at HIIT.
Last updated on 17 Dec 2007 by Martti Mäntylä - Page created on 14 Dec 2007 by Teija Kujala