CP4SBI: New Framework for Improving Uncertainty Quantification in Simulation-Based Inference
Researchers have introduced CP4SBI, a model-agnostic conformal calibration framework designed to correct miscalibrated posterior approximations in simulation-based inference (SBI). SBI is widely used by experimental scientists to invert complex non-linear models with intractable likelihoods, but its posterior estimates frequently undercover true parameters. Improving calibration reliability directly affects the trustworthiness of scientific conclusions drawn from such methods.
CP4SBI is a new framework that applies local conformal calibration to credible sets produced by simulation-based inference, addressing a known problem where posterior approximations tend to be miscalibrated and thus fail to reliably contain true parameter values. The framework is model-agnostic and offers two variants: calibration via regression trees and CDF-based calibration, both of which provide finite-sample local coverage guarantees. These guarantees hold for a range of scoring functions, including highest posterior density (HPD), symmetric, and quantile-based regions. Experiments conducted on standard SBI benchmarks show that CP4SBI improves uncertainty quantification for neural posterior estimators built on both normalizing flows and score-diffusion modeling. The work was submitted to arXiv in August 2025 and has undergone two subsequent revisions, with the latest version posted in June 2026.
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The paper has not yet undergone formal peer review, as it is a preprint hosted on arXiv.
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- arXiv cs.LGCenter
CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference
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