[NEW SUBMISSION] pocoMC

[minaskar]

Citation information

"pocoMC": {
    "tags": [
        "karamanis2022accelerating",
        "karamanis2022pocomc"
    ],
    "logo": "img/pocoMC.png",
    "language": "Python",
    "category": "Statistics",
    "keywords": [
        "Bayesian inference",
        "sampling",
        "model comparison",
        "evidence",
        "data analysis",
        "mcmc",
        "Markov chain Monte Carlo",
        "Preconditioned Monte Carlo",
        "posterior",
        "likelihood"
    ],
    "description": "A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation",
    "link": "https://pocomc.readthedocs.io",
    "attribution_link": "https://github.com/minaskar/pocomc",
    "zenodo_doi": "",
    "custom_citation": "",
    "dependencies": [
        "numpy",
        "scipy"
    ]
}

BibTeX

@article{karamanis2022accelerating,
    title={Accelerating astronomical and cosmological inference with preconditioned Monte Carlo},
    author={Karamanis, Minas and Beutler, Florian and Peacock, John A and Nabergoj, David and Seljak, Uro{\v{s}}},
    journal={Monthly Notices of the Royal Astronomical Society},
    volume={516},
    number={2},
    pages={1644--1653},
    year={2022},
    publisher={Oxford University Press}
}

@article{karamanis2022pocomc,
    title={pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology},
    author={Karamanis, Minas and Nabergoj, David and Beutler, Florian and Peacock, John A and Seljak, Uros},
    journal={arXiv preprint arXiv:2207.05660},
    year={2022}
}

If you opened this template manually head to The Software Citation Station and use the form on the website to generate text for this issue: check it out here!

[FloorBroekgaarden]

thanks for the suggestion @minaskar - greatly appreciated. I added this with commit 0e20008.

I added the updated reference from ADS:

@article{karamanis2022accelerating, author = {{Karamanis}, Minas and {Beutler}, Florian and {Peacock}, John A. and {Nabergoj}, David and {Seljak}, Uro{\v{s}}}, title = "{Accelerating astronomical and cosmological inference with preconditioned Monte Carlo}", journal = {\mnras}, keywords = {methods: data analysis, methods: statistical, large-scale structure of Universe, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Physics - Computational Physics}, year = 2022, month = oct, volume = {516}, number = {2}, pages = {1644-1653}, doi = {10.1093/mnras/stac2272}, archivePrefix = {arXiv}, eprint = {2207.05652}, primaryClass = {astro-ph.IM}, adsurl = {https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.1644K}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }

and

@article{karamanis2022pocomc, author = {{Karamanis}, Minas and {Nabergoj}, David and {Beutler}, Florian and {Peacock}, John and {Seljak}, Uro{\v{s}}}, title = "{pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology}", journal = {The Journal of Open Source Software}, keywords = {Python, astronomy, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Physics - Computational Physics}, year = 2022, month = nov, volume = {7}, number = {79}, eid = {4634}, pages = {4634}, doi = {10.21105/joss.04634}, archivePrefix = {arXiv}, eprint = {2207.05660}, primaryClass = {astro-ph.IM}, adsurl = {https://ui.adsabs.harvard.edu/abs/2022JOSS....7.4634K}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }