| margarine: | Marginal Bayesian Statistics |
|---|---|
| Authors: | Harry T.J. Bevins |
| Version: | 0.3.0 |
| Homepage: | https://github.com/htjb/margarine |
| Documentation: | https://margarine.readthedocs.io/ |
The software should be installed via the git repository using the following commands in the terminal
git clone https://github.com/htjb/margarine.git # or the equivalent using ssh keys
cd margarine
python setup.py install --useror via a pip install with
pip install margarineNote that the pip install is not always the most up to date version of the code.
margarine is designed to make the calculation of marginal bayesian statistics feasible given a set of samples from an MCMC or nested sampling run.
An example of how to use the code can be found on the github in the jupyter notebook notebook/Tutorial.ipynb, alternatively in the compiled documentation or at here.
The documentation is available at: https://margarine.readthedocs.io/
To compile it locally you can run
cd docs
sphinx-build source html-buildafter cloning the repo and installing the relevant packages.
The software is available on the MIT licence.
If you use the code for academic purposes we request that you cite the following paper and the MaxEnt22 proceedings for which you can use the following bibtex
@ARTICLE{2022arXiv220512841B,
author = {{Bevins}, Harry T.~J. and {Handley}, William J. and {Lemos}, Pablo and {Sims}, Peter H. and {de Lera Acedo}, Eloy and {Fialkov}, Anastasia and {Alsing}, Justin},
title = "{Removing the fat from your posterior samples with margarine}",
journal = {arXiv e-prints},
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Computer Science - Machine Learning},
year = 2022,
month = may,
eid = {arXiv:2205.12841},
pages = {arXiv:2205.12841},
archivePrefix = {arXiv},
eprint = {2205.12841},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220512841B},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}and
@ARTICLE{2022arXiv220711457B,
author = {{Bevins}, Harry and {Handley}, Will and {Lemos}, Pablo and {Sims}, Peter and {de Lera Acedo}, Eloy and {Fialkov}, Anastasia},
title = "{Marginal Bayesian Statistics Using Masked Autoregressive Flows and Kernel Density Estimators with Examples in Cosmology}",
journal = {arXiv e-prints},
keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2022,
month = jul,
eid = {arXiv:2207.11457},
pages = {arXiv:2207.11457},
archivePrefix = {arXiv},
eprint = {2207.11457},
primaryClass = {astro-ph.CO},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220711457B},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}The code requires the following packages to run:
To compile the documentation locally you will need:
To run the test suit you will need:
Contributions and suggestions for areas of development are welcome and can be made by opening a issue to report a bug or propose a new feature for discussion.
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