| Issue |
EPJ Web Conf.
Volume 281, 2023
5th International Workshop on Nuclear Data Covariances (CW2022)
|
|
|---|---|---|
| Article Number | 00019 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/epjconf/202328100019 | |
| Published online | 29 March 2023 | |
https://doi.org/10.1051/epjconf/202328100019
Practicalities of Bayesian network modeling for nuclear data evaluation with the nucdataBaynet package
NAPC-Nuclear Data Section, International Atomic Energy Agency, A-1040 Vienna, Austria
* e-mail: g.schnabel@iaea.org
Published online: 29 March 2023
Bayesian networks are a helpful abstraction in the modelization of the relationships between different variables for the purpose of uncertainty quantification. They are therefore especially well suited for the application to nuclear data evaluation to accurately model the relationships of experimental and nuclear models. Constraints, such as sum rules and the non-negativity of cross sections, can be rigorously taken into account in Bayesian inference within Bayesian networks. This contribution elaborates on the practical aspects of the construction of Bayesian networks with the nucdataBaynet package for the purpose of nuclear data evaluation.
© The Authors, published by EDP Sciences, 2023
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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