A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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A Bregman-divergence generalization of ELPD enables robust predictive model selection by tuning sensitivity to tail mismatch via a parameter β.
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Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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Robust Bayesian Predictive Model Selection using Bregman Divergence
A Bregman-divergence generalization of ELPD enables robust predictive model selection by tuning sensitivity to tail mismatch via a parameter β.