https://github.com/florent-leclercq/pyselfi

Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation
https://github.com/florent-leclercq/pyselfi

approximate-bayesian-computation bayesian-data-analysis cosmology galaxy-clustering large-scale-structure likelihood-free-inference

Last synced: 29 days ago
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Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation

• Host: GitHub
• URL: https://github.com/florent-leclercq/pyselfi
• Owner: florent-leclercq
• License: gpl-3.0
• Created: 2019-07-18T11:37:20.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-01-27T15:11:19.000Z (about 3 years ago)
• Last Synced: 2025-09-23T18:52:58.444Z (5 months ago)
• Topics: approximate-bayesian-computation, bayesian-data-analysis, cosmology, galaxy-clustering, large-scale-structure, likelihood-free-inference
• Language: Jupyter Notebook
• Homepage: http://pyselfi.florent-leclercq.eu/
• Size: 44.7 MB
• Stars: 10
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# pySELFI #

[![arXiv](https://img.shields.io/badge/astro--ph.CO-arxiv%3A1902.10149-B31B1B.svg?style=flat)](https://arxiv.org/abs/1902.10149)

[![arXiv](https://img.shields.io/badge/astro--ph.CO-arxiv%3A2209.11057-B31B1B.svg?style=flat)](https://arxiv.org/abs/2209.11057)

[![GitHub version](https://img.shields.io/github/tag/florent-leclercq/pyselfi.svg?label=version)](https://github.com/florent-leclercq/pyselfi)

[![GitHub commits](https://img.shields.io/github/commits-since/florent-leclercq/pyselfi/v2.0.svg)](https://github.com/florent-leclercq/pyselfi/commits)

[![DOI](https://zenodo.org/badge/197575311.svg)](https://zenodo.org/badge/latestdoi/197575311)

[![GPLv3 license](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/florent-leclercq/pyselfi/blob/master/LICENSE)

[![PyPI version](https://badge.fury.io/py/pyselfi.svg)](https://badge.fury.io/py/pyselfi)

[![Docs](https://readthedocs.org/projects/pyselfi/badge/)](http://pyselfi.readthedocs.io/en/latest/)

[![Website florent-leclercq.eu](https://img.shields.io/website-up-down-green-red/http/pyselfi.florent-leclercq.eu.svg)](http://pyselfi.florent-leclercq.eu/)

Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation.

## Documentation ##

The code's homepage is [https://pyselfi.florent-leclercq.eu](https://pyselfi.florent-leclercq.eu). The documentation is available on readthedocs at [https://pyselfi.readthedocs.io/](https://pyselfi.readthedocs.io/). Limited user-support may be asked from the main author, Florent Leclercq.

## Contributors ##

* Florent Leclercq, florent.leclercq@iap.fr

## Reference ##

To acknowledge the use of pySELFI in research papers, please cite its [doi:10.5281/zenodo.3341588](https://doi.org/10.5281/zenodo.3341588) (or for the latest version, see the badge above), as well as the papers [Leclercq et al. (2019)](https://arxiv.org/abs/1902.10149) and [Leclercq (2022)](https://arxiv.org/abs/2209.11057):

* *Primordial power spectrum and cosmology from black-box galaxy surveys*


F. Leclercq, W. Enzi, J. Jasche, A. Heavens


MNRAS 490, 4237 (2019), arXiv:1902.10149 [astro-ph.CO] [ADS] [pdf]

* *Simulation-based inference of Bayesian hierarchical models while checking for model misspecification*


F. Leclercq


Proceedings of the 41st International Conference on Bayesian and Maximum Entropy methods in Science and Engineering (MaxEnt2022), 18-22 July 2022, Paris, France


	Physical Sciences Forum 5, 4 (2022), arXiv:2209.11057 [astro-ph.CO] [ADS] [pdf]

        @ARTICLE{pySELFI1,

            author = {{Leclercq}, Florent and {Enzi}, Wolfgang and {Jasche}, Jens and {Heavens}, Alan},

            title = "{Primordial power spectrum and cosmology from black-box galaxy surveys}",

            journal = {\mnras},

            keywords = {methods: statistical, cosmological parameters, large-scale structure of Universe, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},

            year = "2019",

            month = "Dec",

            volume = {490},

            number = {3},

            pages = {4237-4253},

            doi = {10.1093/mnras/stz2718},

            archivePrefix = {arXiv},

            eprint = {1902.10149},

            primaryClass = {astro-ph.CO},

            adsurl = {https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.4237L},

            }

        @ARTICLE{pySELFI2,

            author = {{Leclercq}, Florent},

            title = "{Simulation-based inference of Bayesian hierarchical models while checking for model misspecification}",

            journal = {Physical Sciences Forum},

            keywords = {Statistics - Methodology, Astrophysics - Instrumentation and Methods for Astrophysics, Mathematics - Statistics Theory, Quantitative Biology - Populations and Evolution, Statistics - Machine Learning},

            year = "2022",

            month = "Sep",

            volume = {5},

            pages = {4},

            doi = {10.3390/psf2022005004},

            archivePrefix = {arXiv},

            eprint = {2209.11057},

            primaryClass = {stat.ME},

            adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220911057L},

            }

## License ##

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. By downloading and using pySELFI, you agree to the [LICENSE](https://github.com/florent-leclercq/pyselfi/blob/master/LICENSE), distributed with the source code in a text file of the same name.