# 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)
# 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
return results if name == " main ": # Load your inference data # idata = az.from_netcdf("my_model.nc") # For demo, create dummy data import pymc as pm with pm