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Exact Sampling of Gibbs Measures with Estimated Losses
A popular strategy for ameliorating some of the shortcomings of Bayesian posterior inference is to instead target a Gibbs measure based on losses that connect a parameter of interest to observed data, and which are known to be robust to misspecification. Existing theory for these procedures treats t
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