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arXiv Machine Learning By Ryan Heejoon Byun, Dayeon Yoon, Won Chang

Prediction Uncertainty Controlled Computer Model Calibration

Read the original on arXiv Machine Learning →

The paper introduces a two‑layer framework for computer‑model calibration that directly controls predictive miscoverage. First, a conditional flow‑matching model learns an amortized posterior for calibration parameters using simulator outputs perturbed with Gaussian‑process discrepancies. Second, conformal risk control arranges posterior draws into nested candidate sets and selects the smallest set whose fixed‑rank pointwise bands achieve a prescribed time‑averaged miscoverage on a synthetic calibration sample. The method yields bands that are about 20% the width of traditional KOH‑MCMC bands while maintaining near‑nominal miscoverage, and when applied to an age‑structured SEIR model of the 2010 Malawi measles outbreak, the bands are sharper and highlight structural inadequacies in the mechanistic model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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