pocoMC#

pocoMC is a Python implementation of Preconditioned Monte Carlo (PMC). Using pocoMC one can perform Bayesian inference, including model comparison, for challenging scientific problems. pocoMC’s sophisticated normalizing flow preconditioning procedures enables efficient sampling from highly correlated posterior distributions. pocoMC is designed to excel in demanding parameter estimation problems that include multimodal and highly non–Gaussian target distributions.

Where to start?

🖥 A good place to get started is with the Installation and then the Quickstart guide. If you are not familiar with Bayesian inference have a look at the Background.

📖 For more details, check out the Advanced Guide and Tutorials, including the full API Reference documentation.

💡 If you’re running into problems getting pocoMC to do what you want, first check out the FAQ page, for some general tips and tricks.

🐛 If FAQ doesn’t solve your problems, or if you find bugs, then head on over to the GitHub issues page.

👈 Check out the sidebar to find the full table of contents.

Attribution & Citation#

Please cite the following if you find this code useful in your research. The BibTeX entries for the papers are:

@article{karamanis2022accelerating,
    title={Accelerating astronomical and cosmological inference with preconditioned Monte Carlo},
    author={Karamanis, Minas and Beutler, Florian and Peacock, John A and Nabergoj, David and Seljak, Uro{\v{s}}},
    journal={Monthly Notices of the Royal Astronomical Society},
    volume={516},
    number={2},
    pages={1644--1653},
    year={2022},
    publisher={Oxford University Press}
}

@article{karamanis2022pocomc,
    title={pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology},
    author={Karamanis, Minas and Nabergoj, David and Beutler, Florian and Peacock, John A and Seljak, Uros},
    journal={arXiv preprint arXiv:2207.05660},
    year={2022}
}

Authors & License#

pocoMC is free software made available under the GPL-3.0 License.

Changelog#

1.0.2 (18/02/24)

Minor improvements and bug fixes.

1.0.0 (28/01/24)

First stable release.
Major refactoring of the code.
Added support for multiple normalizing flows through zuko.
Added preconditioned Crank-Nicolson sampler.
Added support for multilayer SMC.

0.2.2 (22/08/22)

Fixed bridge sampling estimator.
Improved likelihood call counter.

0.1.2 (27/07/22)

Bridge sampling estimator for the model evidence.
Added probit transform for bounded parameters.

0.1.11 (12/07/22)

Include saving and loading the state of the sampler. Useful for resuming runs from files.

0.1.0 (12/07/22)

First version