http://staff.utia.cas.cz/studeny/studeny_home.html?q=user_data/studeny/studeny_home.html

Title: Algebraic approach to independence models and learning Bayesian networks
 

Abstract: 
The motivation for the talk is the description of probablistic conditional independence (CI) models
and learning graphical models. First, a quick overview of graphical approaches to the
description of CI structures will be given. Then the idea of algebraic description of CI structures will be
explained. It can be applied to computer testing CI implications using the methods of linear
programming. The rest of the talk will be devoted to a linear algebraic approach to learning Bayesian
networks, which are special graphical models. The core will be a report on recent results related
to the aim to apply the methods of integer programming in this area.