The revision of elicited beliefs on the structure of a bayesian network

The elicitation of prior information may be a resource against the curse of dimensional- ity affecting score-based (Bayesian) structural learning of Bayesian networks. A Bayesian elicitation procedure focused on network features has been recently proposed which is based on a collection of propositions characterizing a-priori plausible structures. In this work we address issues related to revi- sion of the elicited values and we introduce a parameterization derived from log-linear models that may be useful both to assist the expert in the revision of an elicited distribution or in the elicitation of be- liefs characterized by low order interactions among reference features. A case study dealing with breast cancer is shortly reconsidered to illustrate some features of the proposed parameterization. The discus- sion includes issues related to the development of an open source project devoted to the feature-based elicitation.