Related papers: Bayesian Conformal Prediction as a Decision Risk Problem

Bayesian Conformal Prediction as a Decision Risk Problem

URL: http://arxiv.org/abs/2602.03331v1
Date: Tue, 03 Feb 2026 09:58:27 GMT
Title: Bayesian Conformal Prediction as a Decision Risk Problem
Authors: Fanyi Wu, Veronika Lohmanova, Samuel Kaski, Michele Caprio,
Abstract summary: BCP yields prediction sets of comparable size to split conformal prediction.<n>In sparse regression with nominal coverage of 80 percent, BCP achieves 81 percent empirical coverage under a misspecified prior.
Score: 20.21455697379946
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Bayesian posterior predictive densities as non-conformity scores and Bayesian quadrature are used to estimate and minimise the expected prediction set size. Operating within a split conformal framework, BCP provides valid coverage guarantees and demonstrates reliable empirical coverage under model misspecification. Across regression and classification tasks, including distribution-shifted settings such as ImageNet-A, BCP yields prediction sets of comparable size to split conformal prediction, while exhibiting substantially lower run-to-run variability in set size. In sparse regression with nominal coverage of 80 percent, BCP achieves 81 percent empirical coverage under a misspecified prior, whereas Bayesian credible intervals under-cover at 49 percent.

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