Prof Mark Burgman "Uncertainty and robust decision analysis for Bayes nets"

The probabilities in Bayes nets are themselves uncertain, often based on expert judgement or limited, collateral data. It does not seem sensible to specify a distribution for this uncertainty because it requires information about shapes and moments that usually is unavailable. Instead, the uncertainties in a Bayes net may be represented as sets of intervals. Information gap theory provides a platform for interpreting the uncertainties relative to a decision. This approach is illustrated with an example of the management of arboreal mammals in southern NSW forests.