Obtaining a bayesian network from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper, we define an automatic learni...
Bayesian networks are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. We will analyze Bayesian network...
Bayesian Network structures with a maximum in-degree of k can be approximated with respect to a positive scoring metric up to an factor of 1/k. Key words: approximation algorithm,...
The paper present a layered design for a discrete event simulation framework based on the Java programming language. A description of this project’s goals and motivation is foll...
Wolfgang Kreutzer, Jane Hopkins, Marcel van Mierlo
In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and co...
Bart Baesens, Michael Egmont-Petersen, Robert Cast...