pymc-testing

To test PyMC models without slow MCMC sampling, you can replace the sampling function with prior predictive mock sampling. This validates model structure and downstream code quickly without extensive computational resources.

The best way to speed up pytest workflows for Bayesian modeling is using specialized fixtures that handle setup and teardown. This streamlines testing environments by replacing full inference with faster mock sampling.

Yes, you need pytest to validate PyMC model structure using this testing approach. The provided mock sampling utilities and fixtures are specifically designed to execute within the pytest framework.

Yes, you can use prior predictive mock sampling to verify downstream code. By replacing standard MCMC sampling, it generates the necessary model outputs to ensure your downstream logic behaves as expected.

Mock sampling reduces inference time in PyMC by replacing computationally expensive MCMC sampling with prior predictive sampling. This allows quick verification of model specification and functionality without waiting for full chains to converge.

Using mocks for Bayesian model testing validates structure and downstream code but does not verify actual posterior distributions. It should be used to simplify workflows, not to replace full MCMC inference for final statistical validation.