# For demo, create dummy data import pymc as pm with pm.Model(): x = pm.Normal("x") trace = pm.sample(1000, chains=2, return_inferencedata=True)
return results if name == " main ": # Load your inference data # idata = az.from_netcdf("my_model.nc") hmc checker
ess_ratio = ess / total_samples if np.any(ess_ratio < ess_ratio_threshold): results["warnings"].append(f"Low ESS/total_samples (< {ess_ratio_threshold})") # For demo, create dummy data import pymc as pm with pm
# 6. Energy plot check (text summary) if hasattr(inference_data, "sample_stats") and hasattr(inference_data.sample_stats, "energy"): energy = inference_data.sample_stats.energy.values # simple check: coefficient of variation across chains chain_means = energy.mean(axis=1) cv = np.std(chain_means) / np.mean(chain_means) if cv > 0.1: results["warnings"].append(f"Energy means vary across chains (CV={cv:.3f})") # For demo
# 3. Divergent transitions if hasattr(inference_data, "sample_stats"): diverging = inference_data.sample_stats.diverging.values div_frac = np.mean(diverging) if div_frac > max_divergent_fraction: results["failures"].append(f"Divergent fraction = {div_frac:.3f} > {max_divergent_fraction}") results["passed"] = False elif div_frac > 0: results["warnings"].append(f"Some divergent transitions ({div_frac:.3f})")