{
  "abstract": "When computer codes model complex physical systems, calibration alone is insufficient; one must also assess whether a discrepancy term is needed. In this paper, we study computer code validation through a Bayesian mixture approach that compares a pure-code model with a discrepancy-corrected model. The method relies on the posterior distribution of a mixture weight, which measures the relative support of the two competing distributions. Under the assumption that the code is linear in the calibration parameters, or can be well approximated by a linear surrogate, we show that mixture component-shared parameters can be used to combine flexible modeling, even when noninformative priors are assigned to some common parameters. Inference is performed using a Metropolis-within-Gibbs algorithm. In addition, we introduce a thresholded allocation rule that complements the global mixture weight by providing a local diagnostic of where the discrepancy-corrected component is truly needed along the input domain. Beyond global model comparison, the proposed approach is also able to perform local model discrimination by identifying where the discrepancy-corrected component is truly needed along the input domain.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.27619",
  "author_total": 4,
  "authors": [
    {
      "name": "Negar Soleimani"
    },
    {
      "name": "Pierre Barbillon"
    },
    {
      "name": "Kaniav Kamary"
    },
    {
      "name": "Merlin Keller"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ezev9t2n0zb4c.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.27619",
  "fulltext_url": null,
  "id": "ezev9t2n0zb4c",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.27619",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1804|Probabilistic and Robust Engineering Design"
    ]
  ],
  "openalex": "W7214229974",
  "pdf_url": "https://arxiv.org/pdf/2609.27619",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ezev9t2n0zb4c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:06Z",
      "native_id": "2609.27619",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.27619"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Computer code validation via mixture model estimation",
  "title_text": "Computer code validation via mixture model estimation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:06Z",
  "url": "https://research.draconitic.com/paper/ezev9t2n0zb4c",
  "venue": "arXiv",
  "versions": []
}