{
  "abstract": "Bayes factors are a widely used model comparison criterion, but are often quite challenging and computationally expensive to estimate well. We propose a variation of the model-switch thermodynamic integration method for estimating Bayes factors that decreases the variance of the estimator and essentially eliminates integration error. We choose samples whose density in $β$ is proportional to the standard deviation of previously drawn samples. Rather than averaging many samples at each $β$, we use a very dense set of $β$ values, collecting one sample at each in order of increasing $β$ and one sample at each in order of decreasing $β$ to minimize \"thermic lag\". We study our procedure with a toy model (based loosely on some signal recovery problems). In this model we find that adaptive sampling gives us a 2.7-fold decrease in the spread of the estimated Bayes factor. We test our procedure both drawing directly from the posterior distribution and using a Markov Chain Monte Carlo sampler.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.24354",
  "author_total": 3,
  "authors": [
    {
      "name": "Amanda Malnati"
    },
    {
      "name": "Megan Kralj"
    },
    {
      "name": "Ken D. Olum"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5k337awgnzbbw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.24354",
  "fulltext_url": null,
  "id": "5k337awgnzbbw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.24354",
  "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",
      "astro-ph.HE"
    ],
    [
      "arxiv",
      "physics.data-an"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Markov Chains and Monte Carlo Methods"
    ]
  ],
  "openalex": "W7223064039",
  "pdf_url": "https://arxiv.org/pdf/2610.24354",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5k337awgnzbbw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:04Z",
      "native_id": "2610.24354",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.24354"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.data-an",
      "subfield": "computational"
    }
  ],
  "title": "Adaptive Model-Switch Thermodynamic Integration: A Bayes Factor Estimator",
  "title_text": "Adaptive Model-Switch Thermodynamic Integration: A Bayes Factor Estimator",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:13Z",
  "url": "https://research.draconitic.com/paper/5k337awgnzbbw",
  "venue": "arXiv",
  "versions": []
}