A proposed validation framework for expert elicited Bayesian Networks

► Bayesian Network models are difficult to validate when data are expert elicited. ► We examine the sources of confidence in Bayesian Belief Networks. ► The validation frameworks from related disciplines are reviewed. ► We propose a framework for Bayesian Network validation building on the ideas reviewed. The popularity of Bayesian Network modelling of complex domains using expert elicitation has raised questions of how one might validate such a model given that no objective dataset exists for the model. Past attempts at delineating a set of tests for establishing confidence in an entirely expert-elicited model have focused on single types of validity stemming from individual sources of uncertainty within the model. This paper seeks to extend the frameworks proposed by earlier researchers by drawing upon other disciplines where measuring latent variables is also an issue. We demonstrate that even in cases where no data exist at all there is a broad range of validity tests that can be used to establish confidence in the validity of a Bayesian Belief Network.