Learning Possibilistic Networks from Data

. We introduce a method for inducing the structure of (causal) possibilistic networks from databases of sample cases. In comparison to the construction of Bayesian belief networks, the proposed framework has some advantages, namely the explicit consideration of imprecise (set-valued) data, and the realization of a controlled form of information compression in order to increase the efficiency of the learning strategy as well as approximate reasoning using local propagation techniques. Our learning method has been applied to reconstruct a non-singly connected network of 22 nodes and 24 arcs without the need of any a priori supplied node ordering. 1 Introduction Bayesian networks provide a well-founded normative framework for knowledge representation and reasoning with uncertain, but precise data. Extending pure probabilistic settings to the treatment of imprecise (set-valued) information usually restricts the computational tractability of the corresponding inference mechanisms. It is t..