Hmc Checker ((hot)) Guide
# 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
# 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})") hmc checker
# 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})") rhat_threshold): results["failures"].append(f"R-hat >