Defines the L² over Wasserstein space to equip random probability measures with inherited Riemannian geometry, enabling statistical convergence results and Bayesian posterior consistency in the Wasserstein topology.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
$L^2$ over Wasserstein: Statistical Analysis for Optimal Transport
Defines the L² over Wasserstein space to equip random probability measures with inherited Riemannian geometry, enabling statistical convergence results and Bayesian posterior consistency in the Wasserstein topology.
-
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.