{
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  "date_created": "2026-03-02T18:00:43.257000Z",
  "date_modified": "2026-03-02T18:00:43.257000Z",
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  "record": {
    "abstract": "This report introduces general ideas and some basic methods of the Bayesian\nprobability theory applied to physics measurements. Our aim is to make the\nreader familiar, through examples rather than rigorous formalism, with concepts\nsuch as: model comparison (including the automatic Ockham\u0027s Razor filter\nprovided by the Bayesian approach); parametric inference; quantification of the\nuncertainty about the value of physical quantities, also taking into account\nsystematic effects; role of marginalization; posterior characterization;\npredictive distributions; hierarchical modelling and hyperparameters; Gaussian\napproximation of the posterior and recovery of conventional methods, especially\nmaximum likelihood and chi-square fits under well defined conditions; conjugate\npriors, transformation invariance and maximum entropy motivated priors; Monte\nCarlo estimates of expectation, including a short introduction to Markov Chain\nMonte Carlo methods.",
    "arxiv_id": "physics/0304102",
    "authors": [
      "G. D\u0027Agostini"
    ],
    "categories": [
      "physics.data-an"
    ],
    "doi": "10.1088/0034-4885/66/9/201",
    "journal_ref": "Rept.Prog.Phys. 66 (2003) 1383-1420",
    "title": "Bayesian Inference in Processing Experimental Data: Principles and Basic   Applications",
    "url": "https://arxiv.org/abs/physics/0304102"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "1cbe21e6-0a01-435b-b38c-d218f203031d",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}