{
  "schemaVersion": 1,
  "lessonId": "probability-04",
  "contentHash": "6376255cbf8b00058c317b070f37c405c6d1a3d223f63852cccd9c0bcb42a727",
  "executedAt": "2026-10-20T10:48:56.444623+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "VI mean: [ 0.99999994 -0.99999994] variances: [0.36000016 0.36000016] remaining KL: 0.5108256340026855\nsynthetic nominal-95% interval coverage: 0.2222222238779068\nstandardized residuals: [12.  5.  0. -3. -4. -3.  0.  5. 12.]\nVI mean: [ 0.99999994 -0.99999994] variances: [0.36000016 0.36000016] remaining KL: 0.5108256340026855\nsynthetic nominal-95% interval coverage: 0.2222222238779068\nstandardized residuals: [12.  5.  0. -3. -4. -3.  0.  5. 12.]\nMonte Carlo KL: 0.5119153261184692\nmodel self-coverage: 0.9493999481201172\nPASS: probability-04\n",
  "notebookCells": [
    {
      "sourceHash": "7c5e31a2ad2b04715e8a6263aa790a1c95a1580eb8861e9ac3be4eb1c0c01561",
      "outputs": []
    },
    {
      "sourceHash": "26187f75a36d7884d17439bb7b25934d39ec81f982529d42358fdb37a2515a2a",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VI mean: [ 0.99999994 -0.99999994] variances: [0.36000016 0.36000016] remaining KL: 0.5108256340026855\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "7188b59d1bcd885cd9a3b610471f82284d956cb9e8b7e92984b80c21fcd1d49e",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "synthetic nominal-95% interval coverage: 0.2222222238779068\n",
            "standardized residuals: [12.  5.  0. -3. -4. -3.  0.  5. 12.]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "2226d5694e9c8d935837628a3ccabd029bd4e5fb6c42cf637d916646b9d0de67",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "VI mean: [ 0.99999994 -0.99999994] variances: [0.36000016 0.36000016] remaining KL: 0.5108256340026855\n",
            "synthetic nominal-95% interval coverage: 0.2222222238779068\n",
            "standardized residuals: [12.  5.  0. -3. -4. -3.  0.  5. 12.]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "2716126c648cd1950ef847e317abf46a1144c01703b802e22bc28a237ebc7447",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "55523fceeff45b984225f5d41428555a7a195522936d2e4b482a3abb75979163",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Monte Carlo KL: 0.5119153261184692\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "747baf116135ac47b2fa5a8e171722b93de2ebadf7f077d15401e4afdc12039a",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model self-coverage: 0.9493999481201172\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "44010141dc7017ba0b7b2781feb4555c299b6617d46cc722a4d6b75d37cd017b",
      "outputs": []
    },
    {
      "sourceHash": "2a6dd6fc98242226bd843280fe2d3a1992dbb30ba05743752a98f07e397d6837",
      "outputs": []
    },
    {
      "sourceHash": "523771ac16c562e78a4ca9f28e01fa3fd92ed0506371444daf3b1228cac12739",
      "outputs": []
    },
    {
      "sourceHash": "5d99b44de2ee3b10ba2351de2516979cf3055f3215daf1cfb4df2f181b7a97bf",
      "outputs": []
    },
    {
      "sourceHash": "1353dad18bd94f110cc5b68904931b44c6849bbff6c61d404feb88fe786df71e",
      "outputs": []
    },
    {
      "sourceHash": "f8e5f261f602fd758cbeee22a9d7c9ab2db6820e6cd2bd84da3c8c0bfe9cdd0d",
      "outputs": []
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}
