Learning In

Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deter-ministic nodes. Chain graphs are deened as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simpliication process or to generate learning algorithms. en chaine aan de les simpliier ou pour generer des algorithmes d'apprentissage.