{
  "sources": [
    {
      "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/README.md",
      "quote": "seed   perfect-info      gaussian+plain    gaussian+qm       lattice+qm\n10000  448.261189  443.455598  443.522608  443.663520\n10001  576.779199  571.463409  571.623551  571.977293\n10002  531.886497  525.168028  525.865859  526.411631"
    },
    {
      "url": "https://api.imd.fun/artifacts/8a6e303b662e1a5ea9bb47f31acedd3988ef65b3ddb66d074517aa59ad41ca40",
      "quote": "The source is 42,990 bytes, sha256 **`c68887cff61180e12ba2b1ceb333edf76a830f91f63b5d775a8806fb9ec08aca`**."
    },
    {
      "url": "https://api.imd.fun/artifacts/d3c849db2ac31e567b57e52b80b86e8c24c842e5b4125b9d75c78e3cb07115b9",
      "quote": "class TwoComp(QMRef):\n    \"\"\"Bounded two-component posterior; Taylor (gtmcknight) fee tables.\"\"\"\n    DUAL_CUTS = 2"
    },
    {
      "url": "https://api.imd.fun/artifacts/d360bea6c8c982717b7b43a8069a87013169c89ee3e4da25622db4d880b936d4",
      "quote": "sha256 of this file as run: `c5b711a0b4fb4783e8879a484bf87a427171f4a11aa5eb5ec3513c24ead1a796`; of `twocomp.py` as extracted: `9d3c1833321493fb51bbde4e1b4dabc2f8978bd6d774ee2af8dc8aed13f0f0af`."
    },
    {
      "url": "https://api.imd.fun/artifacts/17d249ab0c33fc44931d9b105cf3f9e3846a560bd518b67310c2a56defacdecc",
      "quote": "It is 10,167 bytes, sha256 `3ac7cb8811ecfb6a17f499aa4e9b70dd55f7fb6432b3aec3b904e11a3dece38e`."
    },
    {
      "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/qmdp.py",
      "quote": "Uncertainty-aware quote tables from the solved perfect-information values (QMDP step).\n\nFor a belief  ln p = estimate + e,  e ~ N(0, s^2)  the quotes are set once for every hypothesis, so\n    (a, b)* = argmax  sum_e  bel(e) * V_N[phase](w - e, w2(e), a - e, b + e)"
    },
    {
      "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/dp.py",
      "quote": "Average-reward relative value iteration; one sweep = one step of lookahead."
    }
  ]
}