{
  "abstract": "Model discrepancy may prevent a single calibration parameter from reproducing a physical response across the context domain. In addition, uncertainties in observations and contexts affect calibration and must be accounted for and quantified. We construct a family of localized posteriors by weighting observation likelihoods according to their proximity to a target context. Gaussian localization weights incorporate context uncertainty through covariance inflation, with reflection at domain boundaries and an optional correction for uneven context sampling. A conditional normalizing flow approximates the family of posteriors through amortized variational inference. Integrating the localized posteriors under a prescribed context measure gives the marginalized parameter distribution. In a twin experiment with the Branin-Hoo model, we compare global and localized inference under uniform and skewed context distributions. Localization reduces the reported aggregate predictive errors relative to the global posterior. An application to the k-ε turbulence closure in a single-column ocean model, with atmospheric surface forcing as context, shows that localized posterior calibration reduces temperature and zonal-velocity errors relative to deterministic and global-posterior calibration. Both twin experiments are constructed to admit local compensation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.37731",
  "author_total": 5,
  "authors": [
    {
      "name": "Exaucé Luweh Adjim Ngarti"
    },
    {
      "name": "Élise Arnaud"
    },
    {
      "name": "Léo Nicoletti"
    },
    {
      "name": "Victor Trappler"
    },
    {
      "name": "Arthur Vidard"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/26ghewbgmk0mf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.37731",
  "fulltext_url": null,
  "id": "26ghewbgmk0mf",
  "issns": [],
  "landing_url": "https://hal.science/hal-05840255v1",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "hal",
      "0.info"
    ],
    [
      "hal",
      "0.sdu"
    ],
    [
      "hal",
      "0.stat"
    ],
    [
      "hal",
      "1.info.info-ai"
    ],
    [
      "hal",
      "1.sdu.ocean"
    ],
    [
      "hal",
      "1.stat.ml"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://hal.science/hal-05840255/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/26ghewbgmk0mf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:01Z",
      "native_id": "hal-05840255",
      "source": "hal",
      "url": "https://hal.science/hal-05840255v1"
    },
    {
      "first_seen": "2026-10-30T12:36:41Z",
      "native_id": "2610.37731",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.37731"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "hal:1.sdu.ocean",
      "subfield": "oceanography"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Amortized Localized Posterior Inference for Model Calibration with Application to an Ocean Turbulence Closure",
  "title_text": "Amortized Localized Posterior Inference for Model Calibration with Application to an Ocean Turbulence Closure",
  "type": "preprint",
  "updated_at": "2026-11-01T12:21:00Z",
  "url": "https://research.draconitic.com/paper/26ghewbgmk0mf",
  "venue": "HAL",
  "versions": []
}