Facilitating the elicitation of beliefs for use in Bayesian Belief modelling. (December 2019) - Hassall, Kirsty L.; Dailey, Gordon; Zawadzka, Joanna; Milne, Alice E.; Harris, Jim A.; Corstanje, Ron; Whitmore, Andrew P.
Abstract: 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). Highlights: 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.