pySELFI

arXiv1 arXiv2 GitHub version GitHub commits DOI GPLv3 license PyPI version Docs Website florent-leclercq.eu

pySELFI is a statistical software package which implements the Simulator Expansion for Likelihood-Free Inference (SELFI) algorithm.

For more information on the code’s purpose and features, please see its homepage.

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
Simulation-based inference of Bayesian hierarchical models while checking for model misspecification
F. Leclercq
@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},
    }