In our previous research, we showed that robust Bayesian methods can be used in environmental modeling to define a set of probability distributions for key parameters that captures the effects of expert disagreement, ambiguity, or ignorance.
This entire set can then be updated against data using Bayes? theorem to investigate the degree to which aleatory and/or epistemic uncertainty are reduced through additional observations.
Further work is required to clarify the methods of selecting the appropriate set definitions in real-world applications.