RT Journal Article
T1 Reusability of Bayesian Networks case studies: a survey
A1 Babakov, Nikolay
A1 Sivaprasad, Adarsa
A1 Reiter, Ehud
A1 Bugarín-Diz, Alberto
K1 Bayesian networks
K1 Probabilistic graphical models
K1 Reproducibility
K1 Reusability
AB Bayesian Networks (BNs) are probabilistic graphical models used to represent variables and their conditional dependencies, making them highly valuable in a wide range of fields, such as radiology, agriculture, neuroscience, construction management, medicine, and engineering systems, among many others. Despite their widespread application, the reusability of BNs presented in papers that describe their application to real-world tasks has not been thoroughly examined. In this paper, we perform a structured survey on the reusability of BNs using the PRISMA methodology, analyzing 147 papers from various domains. Our results indicate that only 18% of the papers provide sufficient information to enable the reusability of the described BNs. This creates significant challenges for other researchers attempting to reuse these models, especially since many BNs are developed using expert knowledge elicitation. Additionally, direct requests to authors for reusable BNs yielded positive results in only 12% of cases. These findings underscore the importance of improving reusability and reproducibility practices within the BN research community, a need that is equally relevant across the broader field of Artificial Intelligence.
PB Springer Nature
SN 1573-7497
SN 0924-669X
YR 2025
FD 2025-02-07
LK https://hdl.handle.net/10347/41174
UL https://hdl.handle.net/10347/41174
LA eng
NO Babakov, N., Sivaprasad, A., Reiter, E. et al. Reusability of Bayesian Networks case studies: a survey. Appl Intell 55, 417 (2025). https://doi.org/10.1007/s10489-025-06289-5
NO We deeply thank the authors who replied to our requests and assisted in collecting the BNs. This research was funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skodowska-Curie grant agreement No 860621. The paper is also a part of R+D+i project PID2023-149959OA-I00, funded by MCIN/AEI/10.13039/501100011033/ and by the “European Union NextGenerationEU/PRTR”. The authors also acknowledge the support of the Galician Ministry for Education, Universities and Professional Training and the “ERDF A way of making Europe” through grants “Centro de investigación de Galicia accreditation 2024-2027 ED431G- 2023/04” and “Reference Competitive Group accreditation 2022-2025 ED431C 2022/19”.  Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
DS Minerva
RD 28 abr 2026