Penniless propagation in join trees

This paper presents non-random algorithms for approximate computation in Bayesian networks. They are based on the use of probability trees to represent probability potentials, using the Kullback-Leibler cross entropy as a measure of the error of the approximation. Different alternatives are presented and tested in several experiments with difficult propagation problems. The results show how it is possible to find good approximations in short time compared with Hugin algorithm.