Hmc Checker -

The HMC checker or liver function test is used for several reasons:

Hamiltonian Monte Carlo is a powerful algorithm for sampling from complex probability distributions, but it relies on strict mathematical assumptions: hmc checker

# 4. Tree depth if hasattr(inference_data, "sample_stats") and hasattr(inference_data.sample_stats, "tree_depth"): depths = inference_data.sample_stats.tree_depth.values max_depth = np.max(depths) # depends on sampler # typical max depth is 10 at_max = (depths == max_depth).mean() if at_max > max_tree_depth_fraction: results["warnings"].append(f"Frequent max tree depth ({at_max:.2f})") The HMC checker or liver function test is

: Parameters must remain within the valid bounds of their respective distributions. BFMI if hasattr(inference_data

# 5. BFMI if hasattr(inference_data, "sample_stats") and hasattr(inference_data.sample_stats, "bfmi"): bfmi = inference_data.sample_stats.bfmi.values.mean() if bfmi < bfmi_threshold: results["failures"].append(f"BFMI = {bfmi:.2f} < {bfmi_threshold}") results["passed"] = False

# 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})")