simuk 0.2.0

Simulation based calibration and generation of synthetic data.

Simulation Based Calibration

A PyMC and Bambi implementation of the algorithms from:

Sean Talts, Michael Betancourt, Daniel Simpson, Aki Vehtari, Andrew Gelman: “Validating Bayesian Inference Algorithms with Simulation-Based Calibration”, 2018; arXiv:1804.06788

Many thanks to the authors for providing open, reproducible code and implementations in rstan and PyStan (link).

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
   
   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.

   sbc.plot_results()
   

What is going on here?

The paper on the arXiv is very well written, and explains the algorithm quite well.

Morally, the example below is exactly what this library does, but it generalizes to more complicated models:

with pm.Model() as model:
    x = pm.Normal('x')
    pm.Normal('y', mu=x, observed=y)

Then what this library does is compute

with my_model():
    prior_samples = pm.sample_prior_predictive(num_trials)

simulations = {'x': []}
for idx in range(num_trials):
    y_tilde = prior_samples['y'][idx]
    x_tilde = prior_samples['x'][idx]
    with model(y=y_tilde):
        idata = pm.sample()
    simulations['x'].append((idata.posterior['x'] < x_tilde).sum())