Seminar on Algebraic Methods in Graphical Markov Models
Description
Graphical models are models determined by a complex system of multivariate dependencies. Algebraic methods have already been developed to draw inference from models with certain given independence structures. The aim of this seminar is to find algebraic tools that will allow us to work with more general graphical models. The Wishart distribution is the distribution that arises naturally in Gaussian graphical models and will also be the subject of some of our studies.
INVITED SPEAKERS
| R. Butler Colorado State University | P. Kim University of Guelph |
| T. Chang University of Virginia | S. Lajmi Facult� des Sciences de Sfax |
| G. Consonni University of Pavia | J. Madsen University of Copenhagen |
| E. Neher University of Ottawa | T. Gneiting University of Windsor |
| M. Perlman University of Washington | L. Wu University of Guelph |
| C. Wong University of Windsor | F. Matus Academy of Sciences of Czech Republic |
| P. Giudici University of Pavia | M. Srivastava University of Toronto |
| P. Graczyk Universit� d'Angers | D. von Rosen Swedish University of the Agriculture Sciences |
| E. Gutierrez Pena Universidad Nacional Autonoma de Mexico |
A. Hassairi Facult� des Sciences de Sfax |
