A Bregman divergence approach yields a unified calibeating framework for general proper losses, delivering U-calibration and logarithmic regret for Tsallis losses with weaker dimension dependence than prior work.
Annals of the Institute of Statistical Mathematics , volume=
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Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
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Calibeating for general proper losses: A Bregman divergence approach
A Bregman divergence approach yields a unified calibeating framework for general proper losses, delivering U-calibration and logarithmic regret for Tsallis losses with weaker dimension dependence than prior work.
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Concentration and Calibration in Predictive Bayesian Inference
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.