Approaches to Reduce Expert Burden in Bayesian Network Parameterization

Abstract

Bayesian Networks (BNs) are popular models that represent complex relationships between variables, which can be quantified by Conditional Probability Tables (CPTs) in the discrete case. If data are not sufficient, experts can be involved to assess the probabilities in the CPTs through Structured Expert Judgment (SEJ), which is often a burdensome task. To lighten the elicitation burden, several methods have been developed previously to construct CPTs using a limited number of input parameters, such as the Ranked Nodes Method (RNM), InterBeta and Functional Interpolation. These methods are first analyzed theoretically, where limitations and potential improvements are determined, which were used as inspiration to develop extensions to the methods. The methods and newly developed extensions, including "ExtraBeta" and "AutoRNM", were applied to reconstruct fully elicited CPTs. Finally, simulation studies are performed to find best practices for InterBeta. InterBeta with parent weights is determined as the best-performingmethod, and the AutoRNM and ExtraBeta extensions are worth exploring further.