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https://github.com/htjb/margarine

Code to replicate posterior probability distributions with bijectors/KDEs and perform marginal KL/bayesian dimensionality calculations.
https://github.com/htjb/margarine

Last synced: 14 days ago
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Code to replicate posterior probability distributions with bijectors/KDEs and perform marginal KL/bayesian dimensionality calculations.

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README 

        
================================================================

margarine: Posterior Sampling and Marginal Bayesian Statistics

================================================================



Introduction

------------



:margarine: Marginal Bayesian Statistics

:Authors: Harry T.J. Bevins

:Version: 1.2.8

:Homepage:  https://github.com/htjb/margarine

:Documentation: https://margarine.readthedocs.io/



.. image:: https://readthedocs.org/projects/margarine/badge/?version=latest

  :target: https://margarine.readthedocs.io/en/latest/?badge=latest

  :alt: Documentation Status

.. image:: https://mybinder.org/badge_logo.svg

  :target: https://mybinder.org/v2/gh/htjb/margarine/master?labpath=notebook%2FTutorial.ipynb

.. image:: http://img.shields.io/badge/astro.IM-arXiv%3A2205.12841-B31B1B.svg

  :target: https://arxiv.org/abs/2205.12841



Installation

------------



The software should be installed via the git repository using the following

commands in the terminal



.. code:: bash



  git clone https://github.com/htjb/margarine.git # or the equivalent using ssh keys

  cd margarine

  python setup.py install --user



or via a pip install with



.. code:: bash



  pip install margarine



Note that the pip install is not always the most up to date version of the code.



Details/Examples

----------------



`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` or alternatively at

`here `__.



Documentation

-------------



The documentation is available at: https://margarine.readthedocs.io/



To compile it locally you can run



.. code:: bash



  cd docs

  sphinx-build source html-build



after cloning the repo and installing the relevant packages with



.. code:: bash



  pip install sphinx numpydoc sphinx_rtd_theme



Licence and Citation

--------------------



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 `__

If you use the clustering implementation please cite the following

`preprint `__.

You can use the following bibtex



.. code:: bibtex



  @ARTICLE{2023MNRAS.526.4613B,

        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 = "{Marginal post-processing of Bayesian inference products with normalizing flows and kernel density estimators}",

        journal = {\mnras},

      keywords = {methods: data analysis, methods: statistical, cosmic background radiation, dark ages, reionization, first stars, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Computer Science - Machine Learning},

          year = 2023,

          month = dec,

        volume = {526},

        number = {3},

          pages = {4613-4626},

            doi = {10.1093/mnras/stad2997},

  archivePrefix = {arXiv},

        eprint = {2205.12841},

  primaryClass = {astro-ph.IM},

        adsurl = {https://ui.adsabs.harvard.edu/abs/2023MNRAS.526.4613B},

        adsnote = {Provided by the SAO/NASA Astrophysics Data System}

  }



and



.. code:: bibtex



  @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}

  }



and



.. code:: bibtex



  @ARTICLE{2023arXiv230502930B,

        author = {{Bevins}, Harry and {Handley}, Will},

          title = "{Piecewise Normalizing Flows}",

        journal = {arXiv e-prints},

      keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},

          year = 2023,

          month = may,

            eid = {arXiv:2305.02930},

          pages = {arXiv:2305.02930},

            doi = {10.48550/arXiv.2305.02930},

  archivePrefix = {arXiv},

        eprint = {2305.02930},

  primaryClass = {stat.ML},

        adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230502930B},

        adsnote = {Provided by the SAO/NASA Astrophysics Data System}

  }



Requirements

------------



The code requires the following packages to run:



- `numpy `__

- `tensorflow `__

- `scipy `__



To compile the documentation locally you will need:



- `sphinx `__

- `numpydoc `__



To run the test suit you will need:



- `pytest `__



Contributing

------------



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.