{
  "id": "2404.02167",
  "version": "v1",
  "published": "2024-03-26T21:57:00.000Z",
  "updated": "2024-03-26T21:57:00.000Z",
  "title": "A remark on conditional entropy",
  "authors": [
    "Adam Wang"
  ],
  "comment": "Preprint, 5 pages",
  "categories": [
    "cs.IT",
    "math.IT"
  ],
  "abstract": "The following note proves that conditional entropy of a sequence is almost time-reversal invariant, specifically they only differ by a small constant factor dependent only upon the forward and backward models that the entropies are being calculated with respect to. This gives rise to a numerical value that quantifies learnability, as well as a methodology to control for distributional shift between datasets. Rough guidelines are given for practitioners.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-03-26T21:57:00.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "conditional entropy",
      "small constant factor dependent",
      "rough guidelines",
      "time-reversal invariant",
      "quantifies learnability"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}