| Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
|---|---|---|
| Article Number | 06001 | |
| Number of page(s) | 7 | |
| Section | 6 - Physics Analysis | |
| DOI | https://doi.org/10.1051/epjconf/202024506001 | |
| Published online | 16 November 2020 | |
https://doi.org/10.1051/epjconf/202024506001
BAT.jl Upgrading the Bayesian Analysis Toolkit
1
Max Planck Institute for Physics, Munich
2
TU Dortmund University, Dortmund
* e-mail: cornelius.grunwald@tu-dortmund.de
Published online: 16 November 2020
In all but the simplest cases, performing data analysis based on Bayesian reasoning requires the use of advanced algorithms. The Bayesian Analysis Toolkit (BAT) provides a collection of algorithms and methods that facilitate the application of Bayesian statistics to user-defined problems of arbitrary complexity. With BAT.jl, we present a modern rewrite of BAT in the Julia programming language. Through the use of a modular software design that is capable of running parallel and distributed, and by extending the tool with new sampling and integration algorithms, BAT.jl is a high-performance framework for Bayesian inference, meeting the requirements of modern data analysis.
© The Authors, published by EDP Sciences, 2020
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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