pocoMC is a Python package for fast Bayesian posterior and model evidence estimation. It leverages the Preconditioned Monte Carlo (PMC) algorithm, offering significant speed improvements over traditional methods like MCMC and Nested Sampling. Ideal for large-scale scientific problems with expensive likelihood evaluations, non-linear correlations, and multimodality, pocoMC provides efficient and scalable posterior sampling and model evidence estimation. Widely used in cosmology and astronomy, pocoMC is user-friendly, flexible, and actively maintained.

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 Likelihood through Blobs information, as well as the Fitting a model to data and Comparing two models tutorials.

💡 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¶

1.2.6 (20/09/24)

Removed unnecessary log-likelihood evaluations during evidence estimation

1.2.5 (16/09/24)

Removed unnecessary log-likelihood evaluations during MCMC sampling.

1.2.4 (28/08/24)

Fix bug in periodic and reflective parameters.

1.2.3 (27/08/24)

Added support for periodic and reflective parameters.
Changed default normalizing flow to nsf6.

1.2.2 (20/06/24)

Fixed bug in posterior method related to blobs.

1.2.1 (14/06/24)

Added support for log-likelihoods that return -np.inf inside the prior volume.

1.2.0 (11/06/24)

Added MPIPool for parallelization.
Fixed bugs in checkpointing when using MPI in NFS4 and BeeGFS filesystems.
Automatically save final checkpoint file when finishing the run if save_every is not None.
Added option to continue sampling after completing the run.

1.1.0 (31/05/24)

Fix robustness issues with the Crank-Nicolson sampler.
Added predefined normalizing flows.
Added support for derived parameters through the blobs framework.
Added dynamic mode for determining the ESS based on the unique sample size (USS).
Added internal multiprocess pool for parallelization.
Improved documentation and tutorials.

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