[1][2][3][4][5][6] An answer is robust if it does not depend sensitively on the assumptions and calculation inputs on which it is based.
[7] In a robust Bayes approach, a standard Bayesian analysis is applied to all possible combinations of prior distributions and likelihood functions selected from classes of priors and likelihoods considered empirically plausible by the analyst.
[8] Some analysts also suggest that robust methods extend the traditional Bayesian approach by recognizing incertitude as of a different kind of uncertainty.
For example, some criticize methods that must assume the analyst is "omniscient" about certain facts such as model structure, distribution shapes and parameters.
Because such facts are themselves potentially in doubt, an approach that does not rely too sensitively on the analysts getting the details exactly right would be preferred.