BibTeX

@article{2508.17077v2,
Author        = {Luben M. C. Cabezas and Vagner S. Santos and Thiago R. Ramos and Pedro L. C. Rodrigues and Rafael Izbicki},
Title         = {CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based
  Inference},
Eprint        = {2508.17077v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Current experimental scientists have been increasingly relying on
simulation-based inference (SBI) to invert complex non-linear models with
intractable likelihoods. However, posterior approximations obtained with SBI
are often miscalibrated, causing credible regions to undercover true
parameters. We develop $\texttt{CP4SBI}$, a model-agnostic conformal
calibration framework that constructs credible sets with local Bayesian
coverage. Our two proposed variants, namely local calibration via regression
trees and CDF-based calibration, enable finite-sample local coverage guarantees
for any scoring function, including HPD, symmetric, and quantile-based regions.
Experiments on widely used SBI benchmarks demonstrate that our approach
improves the quality of uncertainty quantification for neural posterior
estimators using both normalizing flows and score-diffusion modeling.},
Year          = {2025},
Month         = {Aug},
Url           = {http://arxiv.org/abs/2508.17077v2},
File          = {2508.17077v2.pdf}
}