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Infinitesimal sensitivity of posterior distributions
Authors:Fabrizio Ruggeri  Larry Wasserman
Abstract:
We measure the local sensitivity of a posterior expectation with respect to the prior by computing the norm of the Fréchet derivative of the posterior with respect to the prior over several different classes of measures. We compute the derivative of the posterior upper expectation when the prior varies in a restricted ?-contamination class. A bound on the global sensitivity of a class of priors is obtained. As an application, we show that of all sets with posterior probability 1 — α, the likelihood region minimizes the norm of the Fréchet derivative over the ?-contamination class and so is, in some sense, the most robust region with this posterior probability. But there exist counterexamples to this result for other classes of priors.
Keywords:Bayesian robustness  Fré  chet derivatives  likelihood regions
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