arviz-stats

import arviz_stats as azs import arviz_base as azb import numpy as np # Load example data (InferenceData object) data = azb.load_arviz_data("centered_eight") # Compute summary statistics print("\n--- Summary Statistics ---") summary_df = azs.summary(data, var_names=["mu", "tau"]) print(summary_df) # Compute a specific metric, e.g., Root Mean Square Error (RMSE) print("\n--- RMSE Metric ---") # For metrics, ensure you have posterior_predictive and observed_data in your InferenceData # For simplicity here, we'll demonstrate a direct array calculation if data was available # In a real scenario, `data` should have `posterior_predictive` and `observed_data` groups. # Example: azs.metrics(data, kind="rmse") # Let's simulate a simple array for mode calculation as per docs for demonstration rand_data = np.random.normal(loc=5, scale=2, size=1000) mode_val = azs.mode(rand_data) print(f"Mode of simulated data: {mode_val}")