Treffer: Practicalities of Bayesian network modeling for nuclear data evaluation with the nucdataBaynet package.

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. [ABSTRACT FROM AUTHOR]

Copyright of EPJ Web of Conferences is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)