A parallel algorithm for building possibilistic causal networks

Abstract

Among the several representations of uncertainty, possibility theory allows also for the management of imprecision coming from data. Domain models with inherent uncertainty and imprecision can be represented by means of possibilistic causal networks that, the possibilistic counterpart of Bayesian belief networks. Only recently the definition of possibilistic network has been clearly stated and the corresponding inference algorithms developed. However, and in contrast to the corresponding developments in Bayesian networks, learning methods for possibilistic networks are still few. We present here a new approach that hybridizes two of the most used approaches in uncertain network learning: those methods based on conditional dependency information and those based on information quality measures. The resulting algorithm, POSSCAUSE, admits easily a parallel formulation. In the present paper POSSCAUSE is presented and its main features discussed together with the underlying new concepts used.