On Bayesian robustness with the epsilon-contamination class of priors

The problem of measuring the Bayesian robustness when prior distributions are epsilon-contaminated is considered. The total variation metric in the space of the posterior distributions as a global measure of robustness is discussed. An upper bound for the measure when the prior distributions vary in an epsilon-contamination class is given. Examples are presented.