Reusability of Bayesian Networks case studies: a survey

作     者：Babakov, Nikolay Sivaprasad, Adarsa Reiter, Ehud Bugarin-Diz, Alberto 

作者机构：Univ Santiago Compostela Ctr Singular Invest Tecnoloxias Intelixentes CiTIU Santiago De Compostela 15782 Spain Univ Aberdeen Dept Comp Sci Aberdeen Scotland 

出 版 物：《APPLIED INTELLIGENCE》 (Appl Intell)

年 卷 期：2025年第55卷第6期

页      面：1-25页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie grant MCIN/AEI [PID2023-149959OA-I00] European Union NextGenerationEU/PRTR Galician Ministry for Education, Universities and Professional Training ERDF A way of making Europe through grants "Centro de investigacion de Galicia accreditation" [ED431G-2023/04] Reference Competitive Group accreditation [ED431C 2022/19] 

主　　题：Bayesian networks Probabilistic graphical models Reproducibility Reusability 

摘      要：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.