{
  "schema": "ct-poisson-em-supplement/v1",
  "config": {
    "trials": 10000,
    "iterations": 1000,
    "seed": 20260929,
    "defaults": {
      "trials": 10000,
      "iterations": 1000,
      "seed": 20260929
    },
    "nondefault_settings": {},
    "gains": [
      0.1,
      1.0,
      5.0,
      10.0
    ],
    "rng": "numpy.random.default_rng(seed); one Poisson batch per gain, in gain order",
    "bit_generator": "PCG64",
    "numpy_version": "2.3.5",
    "python_version": "3.12.14",
    "mc_checkpoints": [
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      20,
      200,
      1000
    ],
    "init_floor": 1e-08,
    "loglik_rtol": 1e-09,
    "converged_threshold": 1e-06,
    "percentile_method": "numpy default (linear)",
    "note": "elapsed time is printed to the console only, so this file is deterministic"
  },
  "model": {
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    "rank": 9,
    "condition_number_2norm": 2.545463161176507,
    "column_sensitivities": [
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  "definitions": {
    "mse_base_units": "mean over trials of mean_j (X_j/gain - b_j)^2  (= MSE / gain^2)",
    "mse_base_units_mcse": "std(per-trial loss, ddof=1) / sqrt(trials)",
    "crlb_base_units": "trace(F^-1) / (9 gain^2), F = A^T diag(1/(A x_true)) A",
    "crlb_param_units": "trace(F^-1) / 9",
    "bias_base_units": "mean over trials of X/gain, minus b",
    "bias_sq_base_units": "||bias||^2 / 9",
    "variance_base_units": "mean_j var(X_j/gain, ddof=0); bias_sq + variance = mse",
    "relative_change": "||X_k - X_{k-1}|| / max(||X_{k-1}||, tiny), per trial",
    "exact_loglik": "sum_i y_i log mu_i - mu_i - lgamma(y_i + 1), 0 log mu := 0",
    "iteration_0": "clipped least-squares initialization, before any EM step"
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  "method_notes": [
    "Predefined A from main.m is used; random_model is not called.",
    "Initialization changed from legacy inv(A'A)A'y to np.linalg.lstsq clipped to >= 1e-8.",
    "No floor is added to means or parameters during EM iterations.",
    "Log likelihood is exact (lgamma), replacing the legacy Stirling approximation.",
    "CRLB uses the true x; legacy code evaluated it at the current estimate.",
    "Normalization is by gain^2; legacy code divided by the gain index squared."
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      "max_relative_deviation": 0.0,
      "passed": true
    },
    "vectorized_vs_scalar_update": {
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