Hmc Checker < EXTENDED ⇒ >

# 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)

print("HMC Checker Report") print("=" * 40) print(f"Overall status: {'✅ PASS' if report['passed'] else '❌ FAIL'}") if report["warnings"]: print("\n⚠️ Warnings:") for w in report["warnings"]: print(f" - {w}") if report["failures"]: print("\n❌ Failures:") for f in report["failures"]: print(f" - {f}") You could also wrap it as: 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

# 1. R-hat rhat = az.rhat(inference_data).to_array().values if np.any(rhat > rhat_threshold): results["failures"].append(f"R-hat > {rhat_threshold} for some parameters") results["passed"] = False # For demo