Algorithm to Transform Dynamic Causality Diagram Into Belief Network

The Dynamic Causality Diagram methodology is a new probabilistic reasoning model based on Belief Network. To some extent, it is similar to the Belief Network in structure. So that knowledge from one can be transformed into the other on some conditions. To begin with, this paper discusses the similarities and differences between them, and finally presents a transformation algorithm from Dynamic Causality Diagram into Belief Network. The algorithm is composed of two parts: a mapping algorithm of structure and a generating algorithm of conditional probability tables.