{
  "annotation_id": "04a2a84e-efc4-43e2-a786-e50e82f2943f",
  "date_created": "2026-03-02T18:01:11.501000Z",
  "date_modified": "2026-03-02T18:01:11.501000Z",
  "file_hash": "3c03f056a17428a65155abd9eb0521ee63101f56efdc33449efb319bcff6cd8f",
  "private": false,
  "record": {
    "abstract": "We show that Skilling\u0027s method of induction leads to a unique general theory\nof inductive inference, the method of Maximum relative Entropy (ME). The main\ntool for updating probabilities is the logarithmic relative entropy; other\nentropies such as those of Renyi or Tsallis are ruled out. We also show that\nBayes updating is a special case of ME updating and thus, that the two are\ncompletely compatible.",
    "arxiv_id": "physics/0608185",
    "authors": [
      "Ariel Caticha",
      "Adom Giffin"
    ],
    "categories": [
      "physics.data-an",
      "cond-mat.stat-mech",
      "cs.IT",
      "math.IT"
    ],
    "doi": "10.1063/1.2423258",
    "title": "Updating Probabilities",
    "url": "https://arxiv.org/abs/physics/0608185"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "828de39a-9c20-48c8-856a-b681dd663f9d",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}