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

Recent work by Phillips et al., and Selby et al., has shown that influence diagram methodology can be a useful analytical tool in reactor safety studies. An influence diagram is a graphical representation of probabilistic dependence within a system or event sequence. Bayesian statistics are employed to transform the relationships depicted in the influence diagram into the correct expression for a desired marginal probability (e.g. the top event). As with fault trees, top-down and bottom-up algorithms have emerged as the dominant methods for quantifying influence diagrams. Purpose of this paper is to demonstrate a potential error in employing the bottom-up algorithm when dealing with interdependencies. In addition, the computing efficiency of both methods is discussed