{
  "annotation_id": "37a4f71f-21f9-4645-99a8-f5e06de0e6e3",
  "date_created": "2026-03-02T18:01:21.784000Z",
  "date_modified": "2026-03-02T18:01:21.784000Z",
  "file_hash": "b8e170a42840d621328341924eef3c322cfe7a038bbf35126435d9552dab56f1",
  "private": false,
  "record": {
    "abstract": "Conventional statistics begins with a model, and assigns a likelihood of\nobtaining any particular set of data. The opposite approach, beginning with the\ndata and assigning a likelihood to any particular model, is explored here for\nthe case of points drawn randomly from a continuous probability distribution. A\nscalar field theory is used to assign a likelihood over the space of\nprobability distributions. The most likely distribution may be calculated,\nproviding an estimate of the underlying distribution and a convenient graphical\nrepresentation of the raw data. Fluctuations around this maximum likelihood\nestimate are characterized by a robust measure of goodness-of-fit. Its\ndistribution may be calculated by integrating over fluctuations. The resulting\nmethod of data analysis has some advantages over conventional approaches.",
    "arxiv_id": "physics/9706015",
    "authors": [
      "Timothy E. Holy"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1103/PhysRevLett.79.3545",
    "title": "The Analysis of Data from Continuous Probability Distributions",
    "url": "https://arxiv.org/abs/physics/9706015"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "f03f813b-4622-474d-bb83-fe8942f0639e",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}