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arXiv Statistics ML By Troy Butler, Tianyi Jiang, Jo\~ao Silva, Harri Hakula, Timothy Wildey

Copula Transformations for Data-Consistent Inversion

Read the original on arXiv Statistics ML →

The paper introduces a copula-based framework to relate Data‑Consistent Inversion (DCI) and its iterative variant (iDCI). By applying Sklar’s theorem, the authors factor the DCI update into marginal and dependence components, showing that any remaining discrepancy after iDCI convergence is fully captured by the copulas of the observed and predicted joint distributions. They prove that an exact copula transformation recovers the original DCI solution and provide convergence results for approximate transformations, supported by numerical examples illustrating adaptive refinement and progressive problem refinement.

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 Statistics ML.

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