{
  "annotation_id": "5c32b571-bade-44fc-9978-35583e0b247e",
  "date_created": "2026-03-02T18:01:21.779000Z",
  "date_modified": "2026-03-02T18:01:21.779000Z",
  "file_hash": "59987fec4a1fcc080ce10788b283fb0d2f0b0ce65408cc2340cc175b41aac803",
  "private": false,
  "record": {
    "abstract": "A parametric method similar to autoregressive spectral estimators is proposed\nto determine the probability density function (pdf) of a random set. The method\nproceeds by maximizing the likelihood of the pdf, yielding estimates that\nperform equally well in the tails as in the bulk of the distribution. It is\ntherefore well suited for the analysis short sets drawn from smooth pdfs and\nstands out by the simplicity of its computational scheme. Its advantages and\nlimitations are discussed.",
    "arxiv_id": "physics/9810018",
    "authors": [
      "T. Dudok de Wit",
      "E. Floriani"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1103/PhysRevE.58.5115",
    "title": "Estimating probability densities from short samples: a parametric   maximum likelihood approach",
    "url": "https://arxiv.org/abs/physics/9810018"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "457d4ae0-6b0c-4af0-8171-57a3d6764891",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}