A Generalized Associative Petri Net for Reasoning
Dong‐Her Shih, Hsiu‐Sen Chiang, Binshan Lin · IEEE Transactions on Knowledge and Data Engineering · 2007
Although Bayesian networks (BNs) are increasingly being used to solve real-world risk problems, their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognizing that it is rarely cost effective to elicit complete sets of probability values. We describe a simple approach to defining NPTs for a large class of commonly occurring nodes (called ranked nodes). The approach is based on the doubly truncated normal distribution with a central tendency that is invariably a type of weighted function of the parent nodes. In extensive real-world case studies, we have found that this approach is sufficient for generating the NPTs of a very large class of nodes. We describe one such case study for validation purposes. The approach has been fully automated in a commercial tool, called AgenaRisk, and is thus accessible to all types of domain experts. We believe that this work represents a useful contribution to the BN research and technology, since its application makes the difference between being able to build realistic BN models and not.