{
  "annotation_id": "d2d2f30c-373d-40ec-afa1-e840cbba9fb0",
  "date_created": "2026-03-02T18:00:49.110000Z",
  "date_modified": "2026-03-02T18:00:49.110000Z",
  "file_hash": "28bd5cb2ce2ed1dc14990a203465090aa49fb6052cca93370fe4f0174b396cc2",
  "private": false,
  "record": {
    "abstract": "Maximum likelihood fits to data can be done using binned data (histograms)\nand unbinned data. With binned data, one gets not only the fitted parameters\nbut also a measure of the goodness of fit. With unbinned data, currently, the\nfitted parameters are obtained but no measure of goodness of fit is available.\nThis remains, to date, an unsolved problem in statistics. Using Bayes\u0027 theorem\nand likelihood ratios, we provide a method by which both the fitted quantities\nand a measure of the goodness of fit are obtained for unbinned likelihood fits,\nas well as errors in the fitted quantities. The quantity, conventionally\ninterpreted as a Bayesian prior, is seen in this scheme to be a number not a\ndistribution, that is determined from data.",
    "arxiv_id": "physics/0401133",
    "authors": [
      "Rajendran Raja"
    ],
    "categories": [
      "physics.data-an"
    ],
    "journal_ref": "ECONF C030908:MOCT003,2003",
    "title": "A Measure of the Goodness of Fit in Unbinned Likelihood Fits; End of   Bayesianism?",
    "url": "https://arxiv.org/abs/physics/0401133"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "501cc1fb-0d73-4d30-b64c-04c8b12d2ce6",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}