Learning from Imprecise Data: Possibilistic Graphical Models

Graphical models—especially probabilistic networks like Bayes networks and Markov networks—are very popular to make reasoning in highdimensional domains feasible. Since constructing them manually can be tedious and time consuming, a large part of recent research has been devoted to learning them from data. However, if the dataset to learn from contains imprecise information in the form of sets of alternatives instead of precise values, this learning task can pose unpleasant problems. In this paper we study an approach to cope with these problems, which is not based on probability theory as the more common approaches like, e.g., expectation maximization, but uses possibility theory as the underlying calculus of a graphical model.