pySELFI

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

Documentation

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

Contributors

• Florent Leclercq, [email protected]

Reference

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

• 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, distributed with the source code in a text file of the same name.