{
  "environment": {
    "recorded": "2026-09-19",
    "git_head": "2ca8b349f0dffccc7950535b1dec30c6abbd7af3",
    "src_differs_from_head": true,
    "src_sha256": "e9b814b881bff1a010c29a1784111a722df3b59fdb861d14a3c117968605b98e",
    "platform": "macOS-26.5-arm64-arm-64bit",
    "machine": "arm64",
    "python": "3.12.9",
    "bayesmith": "0.9.0",
    "jax": "0.11.1",
    "numpy": "2.5.2",
    "numpyro": "0.21.0",
    "blas": "accelerate",
    "jax_enable_x64": true
  },
  "sources": {
    "first_model.py": "2a6043b85aeaac8c9063de6151d527f3dd61b15f7077f36ce3e574382cc47a1d",
    "five_tasks.py": "a7c83345646177ff5cea071df4328281635015890f94cdd2b2861370edda45e7",
    "nonlinear.py": "a46924aa73dbae0cc74b6a5693c5515a103e3e692b543b1afecf1b25aa2bac54"
  },
  "recorder_sha256": "6bc28c1df4c4462538e6135ec48b73a1106ab3e58b1dfb3ff4130b7bb4750eaf",
  "figures": {
    "first-posterior.svg": "0d1f48943b3f84c9f7a8c9eff048a145d99c8f6c7274625f209c01c8fc6832a2",
    "partial-recovery.svg": "99bbd0d4a493a23f329c7c695e5f92764492e68df99d31235701a576c12501fd",
    "predictive-simulation.svg": "9e36515de3ab8da77dad524d8211a4d00e6e76e28658ea47433a26e00613d520",
    "posterior-predictive.svg": "9bc20f80b6f8490d5babbf6ff1b8c90cb857e255c658c28f72a65f505989cd9f",
    "decay-fit.svg": "b44aae54c1889aac39339021eb2fd4592f500fe193e1400f562f31feddb4358c"
  },
  "tables": {
    "first": {
      "columns": [
        {
          "key": "quantity",
          "label": "Quantity"
        },
        {
          "key": "exact",
          "label": "Closed form",
          "format": ".4f"
        },
        {
          "key": "sampled",
          "label": "Recorded draws",
          "format": ".4f"
        }
      ],
      "rows": [
        {
          "quantity": "Posterior mean",
          "exact": 0.9795918367346939,
          "sampled": 0.9815693517750607
        },
        {
          "quantity": "Posterior sd",
          "exact": 0.2857142857142857,
          "sampled": 0.29225689140732225
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      ],
      "settings": {
        "seed": 7,
        "draws": 256
      }
    },
    "workflow": {
      "columns": [
        {
          "key": "result",
          "label": "Result"
        },
        {
          "key": "value",
          "label": "Recorded output"
        }
      ],
      "rows": [
        {
          "result": "PosteriorResult",
          "value": "256 level draws; mean 0.9633"
        },
        {
          "result": "PointEstimateResult",
          "value": "Posterior mean 0.9796"
        },
        {
          "result": "PredictiveResult",
          "value": "Replicated signal shape (256, 3)"
        },
        {
          "result": "SimulationResult",
          "value": "Prior simulation shape (32,)"
        },
        {
          "result": "EvidenceResult",
          "value": "log p(data) = -2.905733"
        },
        {
          "result": "Artifact round trip",
          "value": "Identity preserved (asserted during execution)"
        }
      ],
      "settings": {
        "seed": 7,
        "posterior_draws": 256,
        "prior_draws": 32
      }
    },
    "gate": {
      "columns": [
        {
          "key": "quantity",
          "label": "Quantity"
        },
        {
          "key": "value",
          "label": "Recorded output"
        }
      ],
      "rows": [
        {
          "quantity": "Operational status",
          "value": "evaluated"
        },
        {
          "quantity": "Verdict",
          "value": "pass"
        },
        {
          "quantity": "Diagnostic warning",
          "value": "Estimated shape parameter of Pareto distribution is greater than 0.58 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations."
        }
      ],
      "settings": {
        "key": 90,
        "requested_draws": 512
      }
    },
    "decay": {
      "columns": [
        {
          "key": "quantity",
          "label": "Quantity"
        },
        {
          "key": "value",
          "label": "Recorded output"
        }
      ],
      "rows": [
        {
          "quantity": "Rate mean",
          "value": "0.5988"
        },
        {
          "quantity": "Rate sd",
          "value": "0.0849"
        },
        {
          "quantity": "Backend",
          "value": "numpyro 0.21.0"
        },
        {
          "quantity": "Termination",
          "value": "fixed budget completed; convergence criteria not met"
        }
      ],
      "settings": {
        "compile_key": 1,
        "sample_key": 2,
        "warmup": 100,
        "draws": 100,
        "chains": 1
      }
    }
  },
  "stdout": {
    "first_model.py": "block 0  {level}          GCR exact         structurally certified; numerical check 3 scales x 3 at-points (max 0.00e+00)\n         kappa=49 -> tol=2.0408163e-05, guard reachable, off by default\n         (require_convergence=)\n         exact block ['level']: sigma does not move with the block (0.000e+00 relative,\n         rtol=1.0e-08), so the covariance is fixed and one Wiener solve is exact\nexecution: iid draws, no chain\nPosterior sample shape: (256,)\nPosterior mean estimate: 0.9815693517750607\n",
    "five_tasks.py": "PosteriorResult\nPointEstimateResult\n{'level': array(0.97959184)}\nPredictiveResult\n(256, 3)\nSimulationResult\nEvidenceResult\nlog p(data): -2.905733186581332\nArtifact round trip: identity preserved\n",
    "nonlinear.py": "block 0  {rate}           NUTS\n         'rate': node 'rate' returns LogNormal; the exact linear-Gaussian path needs a\n         diagonal Normal (a Normal, one wrapped by .to_event(...) or .expand(...), or a\n         ComplexNormal)\nexecution: NUTS\nBackendRef(name='numpyro', version='0.21.0')\nTerminationRecord(reason=<TerminationReason.COMPLETED: 'completed'>, iterations=None, message='fixed budget completed; convergence criteria not met')\n"
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  "check_warnings": [
    "Estimated shape parameter of Pareto distribution is greater than 0.58 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations."
  ]
}