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.
, Pati , Debdeep D
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A Bregman-divergence generalization of ELPD enables robust predictive model selection by tuning sensitivity to tail mismatch via a parameter β.
Proves oracle Bernstein-von Mises theorem for fractional posterior under supportwise likelihood assumptions in sparse GLMs with spike-and-slab priors.
citing papers explorer
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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.