Simuk

Simuk is a Python library for simulation-based calibration (SBC) and the generation of synthetic data. Simulation-Based Calibration is a method for validating Bayesian inference by checking whether the posterior distributions align with the expected theoretical results derived from the prior.

Simuk works with PyMC, Bambi and NumPyro models.

Installation

May be pip installed from github:

pip install simuk

Quickstart

1. Define a PyMC or Bambi model. For example, the centered eight schools model:

   import numpy as np
   import pymc as pm
   from arviz_plots import plot_ecdf_pit
   
   data = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
   sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
   
   with pm.Model() as centered_eight:
       mu = pm.Normal('mu', mu=0, sigma=5)
       tau = pm.HalfCauchy('tau', beta=5)
       theta = pm.Normal('theta', mu=mu, sigma=tau, shape=8)
       y_obs = pm.Normal('y', mu=theta, sigma=sigma, observed=data)
   

2. Pass the model to the SBC class, and run the simulations. This will take a while, as it is running the model many times.

   sbc = SBC(centered_eight,
           num_simulations=100, # ideally this should be higher, like 1000
           sample_kwargs={'draws': 25, 'tune': 50})
   
   sbc.run_simulations()
   

   79%|███████▉  | 79/100 [05:36<01:29,  4.27s/it]
   

3. Plot the empirical CDF for the difference between prior and posterior. The lines should be close to uniform and within the oval envelope.

   plot_ecdf_pit(sbc.simulations,
               visuals={"xlabel":False},
   );
   

References

• Talts, S., Betancourt, M., Simpson, D., Vehtari A., and Gelman A. (2018). Validating Bayesian Inference Algorithms with Simulation-Based Calibration.
• Modrák, M., Moon, A, Kim, S., Bürkner, P., Huurre, N., Faltejsková, K., Gelman A and Vehtari, A.(2023). Simulation-based calibration checking for Bayesian computation: The choice of test quantities shapes sensitivity. Bayesian Analysis.
• Säilynoja, T., Marvin Schmitt, Paul-Christian Bürkner and Aki Vehtari (2025). Posterior SBC: Simulation-Based Calibration Checking Conditional on Data.