Reusability of Bayesian Networks case studies: a survey
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 other...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Santiago de Compostela (USC) |
| Repositorio: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglés |
| OAI Identifier: | oai:minerva.usc.gal:10347/41174 |
| Acceso en línea: | https://hdl.handle.net/10347/41174 |
| Access Level: | acceso abierto |
| Palabra clave: | Bayesian networks Probabilistic graphical models Reproducibility Reusability 1209 Estadística |
| Sumario: | 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. |
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