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Auteurs principaux: Cai, Tiffany, Greengard, Philip, Goodrich, Ben, Gelman, Andrew
Format: Preprint
Publié: 2026
Sujets:
Accès en ligne:https://arxiv.org/abs/2603.20068
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author Cai, Tiffany
Greengard, Philip
Goodrich, Ben
Gelman, Andrew
author_facet Cai, Tiffany
Greengard, Philip
Goodrich, Ben
Gelman, Andrew
contents Bayesian inference is often implemented using approximations, which can yield interval estimates that are too narrow, not fully capturing the uncertainty in the posterior distribution. We address the question of how to adjust these approximate posteriors so that they appropriately capture uncertainty. vWe introduce two methods that extend simulation-based calibration checking (SBC) to widen approximate posterior uncertainty intervals to aim for marginal calibration. We demonstrate these methods in several experimental settings, and we discuss the challenge of calibration using posterior inferences and the potential for posterior recalibration of hierarchical models.
format Preprint
id arxiv_https___arxiv_org_abs_2603_20068
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Approximate posterior recalibration
Cai, Tiffany
Greengard, Philip
Goodrich, Ben
Gelman, Andrew
Methodology
Computation
Bayesian inference is often implemented using approximations, which can yield interval estimates that are too narrow, not fully capturing the uncertainty in the posterior distribution. We address the question of how to adjust these approximate posteriors so that they appropriately capture uncertainty. vWe introduce two methods that extend simulation-based calibration checking (SBC) to widen approximate posterior uncertainty intervals to aim for marginal calibration. We demonstrate these methods in several experimental settings, and we discuss the challenge of calibration using posterior inferences and the potential for posterior recalibration of hierarchical models.
title Approximate posterior recalibration
topic Methodology
Computation
url https://arxiv.org/abs/2603.20068