Top-down versus bottom-up processing of influence diagrams in probabilistic analysis

Recent work by Phillips and Selby has shown that influence diagram methodology can be a useful analytical tool in reactor safety studies. In some instances an influence diagram can be used as a graphical representation of probabilistic dependence within a system or event sequence. Under these circumstances, Bayesian statistics is employed to transform the relationships depicted in the influence diagram into the correct expression for a desired marginal probability (e.g. the top node). Top-down and bottom-up algorithms have emerged as the dominant methods for quantifying influence diagrams. The purpose of this paper is to demonstrate a potential error in employing the bottom-up algorithm when dealing with interdependencies