{
  "annotation_id": "c98e90c5-b6f2-4d99-a60e-210c3338cc96",
  "date_created": "2026-03-02T18:01:38.731000Z",
  "date_modified": "2026-03-02T18:01:38.731000Z",
  "file_hash": "5119d4a0d29490f67cf5aeb3821ed1df93b0ec361c2513cd056a25a2ed1127d5",
  "private": false,
  "record": {
    "abstract": "The paper discusses the reconstruction of potentials for quantum systems at\nfinite temperatures from observational data. A nonparametric approach is\ndeveloped, based on the framework of Bayesian statistics, to solve such inverse\nproblems. Besides the specific model of quantum statistics giving the\nprobability of observational data, a Bayesian approach is essentially based on\n\"a priori\" information available for the potential. Different possibilities to\nimplement \"a priori\" information are discussed in detail, including\nhyperparameters, hyperfields, and non--Gaussian auxiliary fields. Special\nemphasis is put on the reconstruction of potentials with approximate\nperiodicity. The feasibility of the approach is demonstrated for a numerical\nmodel.",
    "arxiv_id": "quant-ph/0005122",
    "authors": [
      "J. C. Lemm",
      "J. Uhlig",
      "A. Weiguny"
    ],
    "categories": [
      "quant-ph",
      "cond-mat.stat-mech",
      "physics.data-an"
    ],
    "doi": "10.1007/PL00011103",
    "title": "Bayesian Reconstruction of Approximately Periodic Potentials at Finite   Temperature",
    "url": "https://arxiv.org/abs/quant-ph/0005122"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "f2814fbe-978f-4a1d-9059-3f614830fccf",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}