Operations and evaluation measures for learning possibilistic graphical models Original Research Article

One focus of research in graphical models is how to learn them from a dataset of sample cases. This learning task can pose unpleasant problems if the dataset to learn from contains imprecise information in the form of sets of alternatives instead of precise values. 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. Since the search methods employed in a learning algorithm are relatively independent of the underlying uncertainty or imprecision calculus, we focus on evaluation measures (or scoring functions).