BayesFast

BayesFast is a Python package for efficient Bayesian analysis developed by He Jia and Uros Seljak, which can be orders of magnitude faster than traditional methods, on both posterior sampling and evidence estimation.

For cosmologists, we have an add-on package CosmoFast, which provides several frequently-used cosmological modules.

Both packages are in live development, so the API may be changed at any time. Note that some parts of the code are still experimental. If you find a bug or have useful suggestions, please feel free to open an issue / pull request, or email He Jia. We also have a roadmap for features to implement in the future. Your contributions would be greatly appreciated!

Links

• Website: https://www.bayesfast.org/
• Documentation: https://bayesfast.readthedocs.io/en/latest/
• Source Code: https://github.com/HerculesJack/bayesfast
• Bug Reports: https://github.com/HerculesJack/bayesfast/issues

Installation

We plan to add pypi and conda-forge support later. For now, please install BayesFast from source with:

git clone https://github.com/HerculesJack/bayesfast
cd bayesfast
pip install -e .
# you can drop the -e option if you don't want to use editable mode
# but note that pytest may not work correctly in this case

To check if BayesFast is built correctly, you can do:

pytest # for this you will need to have pytest installed

Dependencies

BayesFast requires python>=3.6, cython, extension-helpers, matplotlib, multiprocess, numdifftools, numpy>=1.17, scikit-learn, scipy>=1.0 and threadpoolctl. Currently, it has been tested on Ubuntu and MacOS, with python 3.6-3.8.

License

BayesFast is distributed under the Apache License, Version 2.0.

Citing BayesFast

If you find BayesFast useful for your research, please consider citing our papers accordingly:

• He Jia and Uros Seljak, BayesFast: A Fast and Scalable Method for Cosmological Bayesian Inference, in prep (for posterior sampling)
• He Jia and Uros Seljak, Normalizing Constant Estimation with Gaussianized Bridge Sampling, AABI 2019 Proceedings, PMLR 118:1-14 (for evidence estimation)