SCHOOL OF COMPUTER SCIENCE AND SOFTWARE ENGINEERING
MONASH UNIVERSITY


TECHNICAL REPORT 2004/159


Parameterising Bayesian Networks: A Case Study in Ecological Risk Assessment

O Woodberry, A E Nicholson, K B Korb and C Pollino

ABSTRACT

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 from 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.