Discovering Structure in Continuous Variables Using Bayesian Networks

We study Bayesian networks for continuous variables using non(cid: 173) linear conditional density estimators. We demonstrate that use(cid: 173) ful structures can be extracted from a data set in a self-organized way and we present sampling techniques for belief update based on Markov blanket conditional density models.

Authors

• Reimar Hofmann
• Volker Tresp Ludwig Maximilian University of Munich, Munich, Germany Munich Center for Machine Learning, Munich, Germany

Keywords

No keywords are indexed for this paper.