Maximum Entropy-Based Quantification for Probability Elicitation in Bayesian Networks

Annet Onnes, Silja Renooij

Abstract: This paper proposes a quantification method to support the elicitation process for Bayesian network construction. The method aims at reducing the number of subjective modelling choices that need to be made to arrive at an initial quantification of a Bayesian network. Our method allows domain experts to express their knowledge in the form of probability constraints. Then, exploiting recent insights concerning the computation of entropy in Bayesian networks, it uses the Maximum Entropy principle to determine a single quantification that makes no assumptions beyond the information provided by the domain experts. The quantification can be used in an iterative probability elicitation process. We provide an overview of our maximum entropy-based quantification method, detail how to express experts’ constraints for this technique for entropy maximisation and illustrate the method using an example.

External IDs:dblp:conf/ecsqaru/OnnesR25