Optimal Expert Knowledge Elicitation for Bayesian Network Structure Identification

Abstract

Bayesian network (BN) has been a popular tool for gaining mechanistic understanding of variables by revealing how the variables influence each other. It has been found very effective in a few studies in quality control and process monitoring. However, for complex problems where the structure of a BN is unknown, a common approach is to learn the BN structure from observational data. A fundamental bottleneck of this approach is that observational data can only be used to discover part of the influential relationships among variables. To overcome this problem, we propose to combine observational data and expert knowledge. To the best of the author's knowledge, our approach is the first of its kind that formulates an experimental design framework to automate the expert elicitation process and collect the most informative expert knowledge, optimally matched to the observational data, to learn the BN structure.