Facilitating the elicitation of beliefs for use in Bayesian Belief modelling

Expert opinion is increasingly being used to inform Bayesian Belief Networks, in particular to define the conditional dependencies modelled by the graphical structure. The elicitation of such expert opinion remains a major challenge due to both the quantity of information required and the ability of experts to quantify subjective beliefs effectively. In this work, we introduce a method designed to initialise conditional probability tables based on a small number of simple questions that capture the overall shape of a conditional probability distribution before enabling the expert to refine their results in an efficient way. These methods have been incorporated into a software Application for Conditional probability Elicitation (ACE), freely available at https://github.com/KirstyLHassall/ACE (Hassall, 2019). •We present an open source Application for Conditional probability Elicitation.•Complex interdependent relationships can be elicited in a semi-automatic approach.•The scoring algorithm and software facilitate expert elicitation for use within BBNs.