Parameterising Bayesian Networks: A Case Study in Ecological Risk Assessment

Most documented Bayesian network (BN) applications have been built through knowledge elicitation from domain experts (DEs). The difficulties involved have led to growing interest in machine learning of BNs from data. There is a further need for combining what can be learned from the data with what can be elicited front DEs. In this paper, we propose a detailed methodology for this combination, specifically for the parameters of a BN. We illustrate the techniques using a case study of an ecological risk assessment (ERA) problem, specifically the Goulburn Catchment (Victoria, Australia) ERA project.