{
  "annotation_id": "25357e94-58e9-4235-bac5-353837cd723c",
  "date_created": "2026-03-02T18:01:21.525000Z",
  "date_modified": "2026-03-02T18:01:21.525000Z",
  "file_hash": "b87e4eb90d11865f1e8c2c23a4c181212e1a50b5cb4c09eb8e609a7f32e49135",
  "private": false,
  "record": {
    "abstract": "It is well known that the output of a Neural Network trained to disentangle\nbetween two classes has a probabilistic interpretation in terms of the\na-posteriori Bayesian probability, provided that a unary representation is\ntaken for the output patterns. This fact is used to make Neural Networks\napproximate probability density functions from examples in an unbinned way,\ngiving a better performace than ``standard binned procedures\u0027\u0027. In addition,\nthe mapped p.d.f. has an analytical expression.",
    "arxiv_id": "physics/9807018",
    "authors": [
      "Lluis Garrido",
      "Aurelio Juste"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1016/S0010-4655(98)00107-6",
    "title": "On the determination of probability density functions by using Neural   Networks",
    "url": "https://arxiv.org/abs/physics/9807018"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ca80a0f4-dde7-47d6-abee-d2a590042154",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}