| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 06004 | |
| Number of page(s) | 8 | |
| Section | Physics Analysis Tools | |
| DOI | https://doi.org/10.1051/epjconf/202429506004 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429506004
Bayesian Methodologies with pyhf
1 University of Wisconsin-Madison, Madison, Wisconsin, USA
2 Technical University of Munich, Munich, Germany
* e-mail: matthew.feickert@cern.ch
** e-mail: lukas.heinrich@cern.ch
*** e-mail: malin.elisabeth.horstmann@cern.ch
Published online: 6 May 2024
bayesian_pyhf is a Python package that allows for the parallel Bayesian and frequentist evaluation of multi-channel binned statistical models. The Python library pyhf is used to build such models according to the HistFactory framework and already includes many frequentist inference methodologies. The pyhf-built models are then used as data-generating model for Bayesian inference and evaluated with the Python library PyMC. Based on Monte Carlo Chain Methods, PyMC allows for Bayesian modelling and together with the arviz library offers a wide range of Bayesian analysis tools.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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