The project aims to extend the work in
Riabiz, M., Chen, W. Y., Cockayne, J., Swietach, P., Niederer, S. A., Mackey, L., Oates, C. J. (2022). Optimal thinning of MCMC output. Journal of the Royal Statistical Society Series B: Statistical Methodology, 84(4), 1059-1081.
by implementing the idea presented in
Fisher, M. A., Oates, C. (2022). Gradient-free kernel Stein discrepancy. arXiv preprint arXiv:2207.02636.
We replicate the results from [1] for the Lotka-Volterra model in code/lotka_volterra/Stein_thinning.ipynb.
code/notebooks/gaussian_mixture/Gaussian_mixture.ipynb demonstrates using gradient-free kernel Stein density as proposed in [2] for a bivariate Gaussian mixture.
The code/notebooks/examples directory also contains several examples of using the relevant Python packages.
To run the code, navigate to the code directory, create and activate a virtual environment and run the following command:
pip install -e .