Parameterising Bayesian networks: a case study in ecological risk assessment
Carmel Pollino, Owen Woodberry, Ann E. Nicholson, Kevin B. Korb · 2005
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 previous work, we proposed a detailed methodology for this combination, specifically for the parameters of a BN. In this paper, we illustrate the techniques using a case study of an ecological risk assessment problem.