Name,Type,Structure,Probabilities,Graph,Area,Nodes,Arcs,Parameters,Avg. Parents,Max Parents,Avg. Levels,Max Levels,Average Markov Blanket,Year,Journal,Reference
adversarialbehavior,Discrete,Synthetic,Synthetic,Generic,Computer Science,4,4,9,1.0,2,2.0,2,2.5,2022,IET Cyber-Physical Systems: Theory & Applications,"Sahu, A., & Davis, K. (2023). Inferring adversarial behaviour in cyber-physical power systems using a Bayesian attack graph approach. IET Cyber-Physical Systems: Theory & Applications, 8(2), 91-108"
agropastoral2,Discrete,Data,Data,Generic,Social Sciences,11,34,109,1.55,2,3.09,5,3.09,2022,PLoS One,"Palacios, O., Barcelo, J. A., & Delgado, R. (2022). Exploring the role of ecology and social organisation in agropastoral societies: A Bayesian network approach. Plos One, 17(10), e0276088."
agropastoral3,Discrete,Data,Data,Generic,Social Sciences,11,30,85,1.36,2,3.09,5,2.73,2022,PLoS One,"Palacios, O., Barcelo, J. A., & Delgado, R. (2022). Exploring the role of ecology and social organisation in agropastoral societies: A Bayesian network approach. Plos One, 17(10), e0276088."
agropastoral4,Discrete,Data,Data,Generic,Social Sciences,21,28,78,0.67,1,2.57,5,1.33,2022,PLoS One,"Palacios, O., Barcelo, J. A., & Delgado, R. (2022). Exploring the role of ecology and social organisation in agropastoral societies: A Bayesian network approach. Plos One, 17(10), e0276088."
algal1,Discrete,Knowledge,Data,Generic,Environmental Science,9,9,27,1.0,3,2.11,3,3.11,2022,Hydrology and Earth System Sciences,"Jackson-Blake, L. A., Clayer, F., Haande, S., Sample, J. E., & Moe, S. J. (2022). Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network. Hydrology and Earth System Sciences, 26(12), 3103-3124."
algalactivity1,Discrete,Data,Data,Generic,Environmental Science,8,10,22,1.25,2,2.0,2,3.25,2023,Water Research,"Rezaabad, M. Z., Lacey, H., Marshall, L., & Johnson, F. (2023). Influence of resampling techniques on Bayesian network performance in predicting increased algal activity. Water Research, 244, 120558"
algalactivity2,Discrete,Data,Data,Generic,Environmental Science,8,15,43,1.88,4,2.0,2,5.5,2023,Water Research,"Rezaabad, M. Z., Lacey, H., Marshall, L., & Johnson, F. (2023). Influence of resampling techniques on Bayesian network performance in predicting increased algal activity. Water Research, 244, 120558"
algorithms3,Discrete,Synthetic,Synthetic,Generic,Computer Science,4,3,8,0.75,2,2.0,2,2.0,2024,Algorithms,"Scutari, M. (2024). Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation. Algorithms, 17(1), 24"
algorithms4,Discrete,Synthetic,Synthetic,Generic,Computer Science,4,4,9,1.0,2,2.0,2,2.5,2024,Algorithms,"Scutari, M. (2024). Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation. Algorithms, 17(1), 24"
APSsystem,Discrete,Knowledge,Knowledge,Generic,Computer Science,10,9,21,0.9,2,2.0,2,2.2,2022,Procedia Computer Science,"Herrmann, J. P., Tackenberg, S., Padoano, E., Hartlief, J., Rautenstengel, J., Loeser, C., & Bohme, J. (2022). An ERP Data Quality Assessment Framework for the Implementation of an APS system using Bayesian Networks. Procedia Computer Science, 200, 194-204."
fake_asia,Discrete,Synthetic,Synthetic,Generic,Medicine,8,8,18,1.0,2,2.0,2,2.5,1988,Journal of the Royal Statistical Society: Series B,"Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society: Series B (Methodological), 50(2), 157-194"
augmenting,Discrete,Knowledge,Knowledge,Generic,Engineering,6,6,53,1.0,3,2.67,3,3.67,2019,International Symposium on Real-Time Distributed Computing,"Ramakrishna, S., Dubey, A., Burruss, M. P., Hartsell, C., Mahadevan, N., Nannapaneni, S., ... & Karsai, G. (2019, May). Augmenting learning components for safety in resource constrained autonomous robots. In 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC) (pp. 108-117). IEEE"
bank,Discrete,Data,Data,Generic,Business,4,5,19,1.25,2,2.5,4,2.5,2024,Data Mining and Knowledge Discovery,"Leonelli, M., & Varando, G. (2024). Structural learning of simple staged trees. Data Mining and Knowledge Discovery, 38(3), 1520-1544"
bankruptcy,Discrete,Fixed,Data,Naive Bayes,Economics,9,8,29,0.89,1,2.67,3,1.78,2007,European Journal of Operational Research,"Sun, L., & Shenoy, P. P. (2007). Using Bayesian networks for bankruptcy prediction: Some methodological issues. European Journal of Operational Research, 180(2), 738-753"
beatles,Discrete,Fixed,Knowledge,Reverse Naive Bayes,Law,5,4,20,0.8,4,2.0,2,4.0,2023,Artificial Intelligence and Law,"Kowalewska, A., & Urbaniak, R. (2023). Measuring coherence with Bayesian networks. Artificial Intelligence and Law, 31(2), 369-395"
blockchain,Discrete,Data,Data,Generic,Social Sciences,12,13,73,1.08,5,2.0,2,5.5,2021,Technological Forecasting and Social Change,"Kamble, S. S., Gunasekaran, A., Kumar, V., Belhadi, A., & Foropon, C. (2021). A machine learning based approach for predicting blockchain adoption in supply chain. Technological Forecasting and Social Change, 163, 120465"
BOPfailure1,Discrete,Knowledge,Expert,Reverse Tree,Engineering,30,29,132,0.97,5,2.0,2,4.27,2023,PLoS One,"Satiarvand, M., Orak, N., Varshosaz, K., Hassan, E. M., & Cheraghi, M. (2023). Providing a comprehensive approach to oil well blowout risk assessment. Plos One, 18(12), e0296086"
BOPfailure2,Discrete,Knowledge,Expert,Reverse Tree,Engineering,46,45,350,0.98,7,2.0,2,5.3,2023,PLoS One,"Satiarvand, M., Orak, N., Varshosaz, K., Hassan, E. M., & Cheraghi, M. (2023). Providing a comprehensive approach to oil well blowout risk assessment. Plos One, 18(12), e0296086"
BOPfailure3,Discrete,Knowledge,Expert,Reverse Tree,Engineering,28,27,92,0.96,5,2.0,2,3.43,2023,PLoS One,"Satiarvand, M., Orak, N., Varshosaz, K., Hassan, E. M., & Cheraghi, M. (2023). Providing a comprehensive approach to oil well blowout risk assessment. Plos One, 18(12), e0296086"
chds,Discrete,Data,Data,Tree,Medicine,4,3,10,0.75,1,2.25,3,1.5,2013,International Journal of Approximate Reasoning,"Barclay, L. M., Hutton, J. L., & Smith, J. Q. (2013). Refining a Bayesian network using a chain event graph. International Journal of Approximate Reasoning, 54(9), 1300-1309"
conasense,Discrete,Knowledge,Knowledge,Generic,Computer Science,4,6,107,1.5,3,3.25,4,3.0,2022,International Symposium on Wireless Personal Multimedia Communications,"Henrique, P. S. R., & Prasad, R. (2022, October). Bayesian Neural Networks for 6G CONASENSE Services. In 2022 25th International Symposium on Wireless Personal Multimedia Communications (WPMC) (pp. 291-296). IEEE"
concrete3,Discrete,Data,Data,Tree,Chemical Engineering,3,2,17,0.67,1,3.33,5,1.33,2023,Expert Systems With Applications,"Wu, P. Y., Johansson, T., Mangold, M., Sandels, C., & Mjornell, K. (2023). Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks. Expert Systems with Applications, 222, 119812"
concrete4,Discrete,Data,Data,Tree,Chemical Engineering,4,3,16,0.75,1,3.0,5,1.5,2023,Expert Systems With Applications,"Wu, P. Y., Johansson, T., Mangold, M., Sandels, C., & Mjornell, K. (2023). Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks. Expert Systems with Applications, 222, 119812"
concrete5,Discrete,Data,Data,Tree,Chemical Engineering,3,2,17,0.67,1,3.33,5,1.33,2023,Expert Systems With Applications,"Wu, P. Y., Johansson, T., Mangold, M., Sandels, C., & Mjornell, K. (2023). Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks. Expert Systems with Applications, 222, 119812"
concrete6,Discrete,Data,Data,Naive Bayes,Chemical Engineering,3,2,13,0.67,1,3.33,5,1.33,2023,Expert Systems With Applications,"Wu, P. Y., Johansson, T., Mangold, M., Sandels, C., & Mjornell, K. (2023). Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks. Expert Systems with Applications, 222, 119812"
concrete7,Discrete,Data,Data,Tree,Chemical Engineering,5,4,57,0.8,1,4.0,5,1.6,2023,Expert Systems With Applications,"Wu, P. Y., Johansson, T., Mangold, M., Sandels, C., & Mjornell, K. (2023). Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks. Expert Systems with Applications, 222, 119812"
constructionproductivity,Discrete,Expert,Expert,Generic,Engineering,18,19,74,1.06,5,2.0,2,3.89,2022,"Engineering, Construction and Architectural Management","Khanh, H. D., & Kim, S. Y. (2022). Construction productivity prediction through Bayesian networks for building projects: Case from Vietnam. Engineering, Construction and Architectural Management, 30(5), 2075-2100"
corrosion,Discrete,Knowledge,Expert,Generic,Engineering,22,48,242,1.09,4,2.55,4,3.91,2023,Engineering Reliability & System Safety,"Dao, U., Sajid, Z., Khan, F., & Zhang, Y. (2023). Dynamic Bayesian network model to study under-deposit corrosion. Reliability Engineering & System Safety, 237, 109370"
covidfear,Discrete,Data,Data,Generic,Psychology,9,6,40,0.67,1,2.78,3,1.33,2024,Applied Intelligence,"Leonelli, M., & Varando, G. (2024). Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear. Applied Intelligence, 54(2), 1734-1750"
covidrisk,Discrete,Data,Data,Generic,Social Sciences,9,13,27,1.44,2,2.0,2,3.11,2022,International Conference on Probabilistic Graphical Models,"Leonelli, M., & Varando, G. (2022, September). Highly efficient structural learning of sparse staged trees. In International Conference on Probabilistic Graphical Models (pp. 193-204). PMLR"
criminal3,Discrete,Data,Data,Generic,Law,8,9,19,1.12,2,2.0,2,2.5,2022,International Conference on Artificial Intelligence and Law,"van Leeuwen, L., Verheij, B., Verbrugge, R., & Renooij, S. (2023, June). Using agent-based simulations to evaluate Bayesian Networks for criminal scenarios. In Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law (pp. 323-332)"
criminal4,Discrete,Data,Data,Generic,Law,8,9,19,1.12,2,2.0,2,2.5,2022,International Conference on Artificial Intelligence and Law,"van Leeuwen, L., Verheij, B., Verbrugge, R., & Renooij, S. (2023, June). Using agent-based simulations to evaluate Bayesian Networks for criminal scenarios. In Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law (pp. 323-332)"
curacao1,Discrete,Expert,Expert,Reverse Tree,Environmental Science,13,12,77,0.92,4,2.38,3,4.0,2024,Environmental Science and Policy,"Steward, R., Chopin, P., & Verburg, P. H. (2024). Supporting spatial planning with a novel method based on participatory Bayesian networks: An application in Curacao. Environmental Science & Policy, 156, 103733"
curacao2,Discrete,Expert,Expert,Reverse Tree,Environmental Science,13,12,114,0.92,5,2.38,3,4.15,2024,Environmental Science and Policy,"Steward, R., Chopin, P., & Verburg, P. H. (2024). Supporting spatial planning with a novel method based on participatory Bayesian networks: An application in Curacao. Environmental Science & Policy, 156, 103733"
curacao3,Discrete,Expert,Expert,Reverse Tree,Environmental Science,19,18,84,0.95,4,2.21,3,4.0,2024,Environmental Science and Policy,"Steward, R., Chopin, P., & Verburg, P. H. (2024). Supporting spatial planning with a novel method based on participatory Bayesian networks: An application in Curacao. Environmental Science & Policy, 156, 103733"
diabetes,Discrete,Data,Data,Generic,Medicine,9,8,18,0.89,2,2.0,2,2.0,2023,Knowledge-Based Systems,"Leonelli, M., Ramanathan, R., & Wilkerson, R. L. (2023). Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package. Knowledge-Based Systems, 278, 110882"
disputed1,Discrete,Knowledge,Knowledge,Generic,Law,11,11,25,1.0,2,2.0,2,2.55,2024,Forensic Science International: Synergy,"Vink, M., de Koeijer, J. A., & Sjerps, M. J. (2024). A template Bayesian network for combining forensic evidence on an item with an uncertain relation to the disputed activities. Forensic Science International: Synergy, 9, 100546"
drainage,Discrete,Knowledge,Expert,Reverse Tree,Engineering,11,10,31,0.91,3,2.0,2,3.27,2024,IEEE Sensors,"Shi, X., Gu, H., & Yao, B. (2024). Fuzzy Bayesian Network Fault Diagnosis Method Based on Fault Tree for Coal Mine Drainage System. IEEE Sensors Journal"
dustexplosion,Discrete,Knowledge,Expert,Generic,Engineering,26,32,142,1.23,3,2.5,5,3.23,2023,Journal of Loss Prevention in the Process Industries,"Pang, L., Zhang, M., Yang, K., & Sun, S. (2023). Scenario derivation and consequence evaluation of dust explosion accident based on dynamic Bayesian network. Journal of Loss Prevention in the Process Industries, 83, 105055"
ecosystem,Discrete,Knowledge,Knowledge,Reverse Tree,Environmental Science,13,12,98,0.92,3,2.69,3,2.92,2022,Ecological Indicators,"Wu, J., Jin, X., Wang, H., & Feng, Z. (2022). Evaluating the supply-demand balance of cultural ecosystem services with budget expectation in Shenzhen, China. Ecological Indicators, 142, 109165"
electricvehicle,Discrete,Knowledge,Expert,Reverse Tree,Engineering,23,22,79,0.96,4,2.0,2,3.83,2022,Mathematics,"Chen, J., Li, K., & Yang, S. (2022). Electric vehicle fire risk assessment based on WBS-RBS and fuzzy BN coupling. Mathematics, 10(20), 3799"
electrolysis,Discrete,Knowledge,Knowledge,Generic,Chemical Engineering,16,18,83,1.12,5,2.0,2,4.88,2023,Journal of Environmental Chemical Engineering,"Liu, Y., Amin, M. T., Khan, F., & Pistikopoulos, E. N. (2023). Safety analysis of proton exchange membrane water electrolysis system. Journal of Environmental Chemical Engineering, 11(5), 110772"
emergency,Discrete,Knowledge,Knowledge,Generic,Engineering,17,16,36,0.94,2,2.0,2,2.24,2024,Reliability Engineering & System Safety,"Guo, J., Ma, S., Zeng, S., Che, H., & Pan, X. (2024). A risk evaluation method for human-machine interaction in emergencies based on multiple mental models-driven situation assessment. Reliability Engineering & System Safety, 110444"
engines,Discrete,Knowledge,Expert,Generic,Engineering,12,18,80,1.5,5,2.0,2,6.17,2024,Ships and Offshore Structures,"Bashan, V., Yucesan, M., Gul, M., & Demirel, H. (2024). A fuzzy Bayesian network risk assessment model for analyzing the causes of slow-down processes in two-stroke ship main engines. Ships and Offshore Structures, 19(5), 670-686."
enrollment,Discrete,Fixed,Data,Naive Bayes,Social Sciences,8,7,33,0.88,1,3.12,5,1.75,2024,Sustainability,"Wang, K., Wang, T., Wang, T., & Cai, Z. (2024). Research on Evaluation Methods for Sustainable Enrollment Plan Configurations in Chinese Universities Based on Bayesian Networks. Sustainability, 16(7), 2998"
firealarm,Discrete,Synthetic,Synthetic,Generic,Engineering,6,5,17,0.83,2,2.17,3,2.0,2002,Journal of Artificial Intelligence Research,"Hei Chan, Adnan Darwiche (2002). When do numbers really matter?. Journal of Artificial Intelligence Research 17 (265-287)"
firerisk,Discrete,Knowledge,Knowledge,Generic,Engineering,23,26,57,1.13,3,2.0,2,2.87,2024,Journal of Theoretical and Applied Information Technology,"Issa, S. K., Bakkali, H., Azmani, A., & Amami, B. (2024). Predictive study of fire risk in building using Bayesian networks. Journal of Theoretical and Applied Information Technology, 102(7)"
foodallergy1,Discrete,Data,Data,Generic,Medicine,8,12,31,1.5,3,2.0,2,3.75,2023,World Allergy Organization Journal,"Belmabrouk, S., Abdelhedi, R., Bougacha, F., Bouzid, F., Gargouri, H., Ayadi, I., ... & Rebai, A. (2023). Prevalence of self-reported food allergy in Tunisia: General trends and probabilistic modeling. World Allergy Organization Journal, 16(9), 100813"
foodallergy2,Discrete,Data,Data,Generic,Medicine,8,8,19,1.0,2,2.0,2,2.25,2023,World Allergy Organization Journal,"Belmabrouk, S., Abdelhedi, R., Bougacha, F., Bouzid, F., Gargouri, H., Ayadi, I., ... & Rebai, A. (2023). Prevalence of self-reported food allergy in Tunisia: General trends and probabilistic modeling. World Allergy Organization Journal, 16(9), 100813"
foodallergy3,Discrete,Data,Data,Generic,Medicine,8,8,17,1.0,2,2.0,2,2.25,2023,World Allergy Organization Journal,"Belmabrouk, S., Abdelhedi, R., Bougacha, F., Bouzid, F., Gargouri, H., Ayadi, I., ... & Rebai, A. (2023). Prevalence of self-reported food allergy in Tunisia: General trends and probabilistic modeling. World Allergy Organization Journal, 16(9), 100813"
fundraising,Discrete,Knowledge,Knowledge,Generic,Business,8,8,25,1.0,3,2.0,2,3.5,2022,Mathematics,"Yang, S., Su, K., Wang, B., & Xu, Z. (2022). A coupled mathematical model of the dissemination route of short-term fund-raising fraud. Mathematics, 10(10), 1709."
gasexplosion,Discrete,Knowledge,Data,Generic,Chemical Engineering,18,23,57,1.28,3,2.0,2,3.78,2023,Processes,"Niu, L., Zhao, J., & Yang, J. (2023). Risk assessment of unsafe acts in coal mine gas explosion accidents based on HFACS-GE and Bayesian networks. Processes, 11(2), 554"
gasifier,Discrete,Knowledge,Expert,Reverse Tree,Chemical Engineering,40,39,159,0.98,5,2.0,2,4.0,2022,Processes,"Liu, Y., Wang, S., Liu, Q., Liu, D., Yang, Y., Dan, Y., & Wu, W. (2022). Failure risk assessment of coal gasifier based on the integration of bayesian network and trapezoidal intuitionistic fuzzy number-based similarity aggregation method (TpIFN-SAM). Processes, 10(9), 1863"
GDIpathway1,Discrete,Knowledge,Data,Generic,Genetics,28,31,128,1.11,6,2.0,2,4.0,2022,IEEE Journal of Biomedical and Health Informatics,"Gupta, S., Vundavilli, H., Osorio, R. S. A., Itoh, M. N., Mohsen, A., Datta, A., ... & Tripathi, L. P. (2022). Integrative network modeling highlights the crucial roles of rho-GDI signaling pathway in the progression of non-small cell lung cancer. IEEE Journal of Biomedical and Health Informatics, 26(9), 4785-4793"
GDIpathway2,Discrete,Knowledge,Data,Generic,Genetics,28,31,128,1.11,6,2.0,2,4.0,2022,IEEE Journal of Biomedical and Health Informatics,"Gupta, S., Vundavilli, H., Osorio, R. S. A., Itoh, M. N., Mohsen, A., Datta, A., ... & Tripathi, L. P. (2022). Integrative network modeling highlights the crucial roles of rho-GDI signaling pathway in the progression of non-small cell lung cancer. IEEE Journal of Biomedical and Health Informatics, 26(9), 4785-4793"
greencredit,Discrete,Data,Data,Generic,Energy,10,12,92,1.2,2,3.0,3,2.8,2023,Journal of Systems Science and Complexity,"Chai, J., Wang, Y., Hu, Y., Zhang, X., & Zhang, X. (2023). The Coupling Relationships and Influence Mechanisms of Green Credit and Energy-Environment-Economy Under China's Goal of Carbon Neutrality. Journal of Systems Science and Complexity, 36(1), 360-374"
humanitarian,Discrete,Data,Data,Generic,Social Sciences,21,20,122,0.95,1,3.0,3,1.9,2022,International Conference on Probabilistic Graphical Models,"Ballester-Ripoll, R., & Leonelli, M. (2022, September). You only derive once (YODO): automatic differentiation for efficient sensitivity analysis in Bayesian networks. In International Conference on Probabilistic Graphical Models (pp. 169-180). PMLR"
hydraulicsystem,Discrete,Fixed,Knowledge,Reverse Naive Bayes,Engineering,4,3,11,0.75,3,2.0,2,3.0,2023,Measurement Science and Technology,"Pan, W. H., Feng, Y. W., Liu, J., & Lu, C. (2023). Operation reliability monitoring towards fault diagnosis of airplane hydraulic system using quick access recorder flight data. Measurement Science and Technology, 34(5), 055111"
inverters,Discrete,Knowledge,Data,Generic,Engineering,29,33,109,1.14,4,2.0,2,4.14,2019,Mathematical Problems in Engineering,"Han, S., He, Y., Zheng, S., & Wang, F. (2019). Intelligent Fault Inference of Inverters Based on a Three-Layer Bayesian Network. Mathematical Problems in Engineering, 2019(1), 3653746."
knowledge,Discrete,Data,Data,Generic,Social Sciences,6,5,12,0.83,2,2.0,2,2.0,2020,Mathematical Problems in Engineering,"Wang, D., AmrilJaharadak, A., & Xiao, Y. (2020). Dynamic knowledge inference based on bayesian network learning. Mathematical Problems in Engineering, 2020(1), 6613896"
lawschool,Discrete,Data,Data,Generic,Social Sciences,10,21,117,2.1,4,2.7,6,4.4,2022,Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,"Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., & Ntoutsi, E. (2022). A survey on datasets for fairness-aware machine learning. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(3), e1452"
lidar,Discrete,Synthetic,Synthetic,Generic,Earth Sciences,9,8,18,0.89,2,2.0,2,2.0,2022,Geospatial Informatics,"Bogart, C., Solorzano, L., & Lam, S. (2022, May). Decision support using SAR and LiDAR machine learning target classification and Bayesian belief networks. In Geospatial Informatics XII (Vol. 12099, pp. 28-36). SPIE"
liquidity,Discrete,Data,Data,Generic,Economics,10,13,29,1.3,3,2.0,2,3.0,2018,Neurocomputing,"Tavana, M., Abtahi, A. R., Di Caprio, D., & Poortarigh, M. (2018). An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking. Neurocomputing, 275, 2525-2554"
lithium,Discrete,Knowledge,Expert,Reverse Tree,Energy,45,44,199,0.98,5,2.0,2,4.44,2023,Sustainability,"Zhang, C., Sun, H., Zhang, Y., Li, G., Li, S., Chang, J., & Shi, G. (2023). Fire accident risk analysis of lithium battery energy storage systems during maritime transportation. Sustainability, 15(19), 14198"
medicaltest,Discrete,Synthetic,Synthetic,Reverse Naive Bayes,Medicine,3,2,6,0.67,2,2.0,2,2.0,2024,arXiv,"Ballester-Ripoll, R., & Leonelli, M. (2024). Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks. arXiv preprint arXiv:2406.05764"
megacities,Discrete,Knowledge,Data,Reverse Tree,Energy,18,17,55,0.94,4,2.0,2,3.44,2023,Journal of Cleaner Production,"Li, H., Huang, W., Qian, Y., & Klemes, J. J. (2023). Air pollution risk assessment related to fossil fuel-driven vehicles in megacities in China by employing the Bayesian network coupled with the Fault Tree method. Journal of Cleaner Production, 383, 135458"
metal,Discrete,Fixed,Expert,Reverse Naive Bayes,Engineering,8,7,263,0.88,7,2.12,3,7.0,2022,Mindanao Journal of Science and Technology,"Akinyemi, O. O., Adeyemi, H. O., Olatunde, O. B., Folorunsho, O., & Musa, M. B. (2022). Bayesian belief network modeling of accident occurrence in metal lathe machining operations. Mindanao Journal of Science and Technology, 20(2)"
navigation,Discrete,Knowledge,Knowledge,Reverse Tree,Engineering,4,3,28,0.75,2,3.0,3,2.0,2023,The Journal of Navigation,"Oliveira, D., Moraes, A., Junior, M. C., & Marini-Pereira, L. (2023). Safety analysis of RNP approach procedure using fusion of FRAM model and Bayesian belief network. The Journal of Navigation, 76(2-3), 286-315"
oildepot,Discrete,Knowledge,Expert,Reverse Tree,Engineering,41,40,137,0.98,5,2.0,2,3.66,2023,Process Safety and Environmental Protection,"Zhou, Q. Y., Li, B., Lu, Y., Chen, J., Shu, C. M., & Bi, M. S. (2023). Dynamic risk analysis of oil depot storage tank failure using a fuzzy Bayesian network model. Process Safety and Environmental Protection, 173, 800-811"
oxygen,Discrete,Knowledge,Expert,Generic,Engineering,31,32,93,1.03,4,2.0,2,3.55,2023,Heliyon,"Laal, F., Hanifi, S. M., Madvari, R. F., Khoshakhlagh, A. H., & Arefi, M. F. (2023). Providing an approach to analyze the risk of central oxygen tanks in hospitals during the COVID-19 pandemic. Heliyon, 9(8)"
phdarticles,Discrete,Data,Data,Generic,Social Sciences,6,4,16,0.67,1,2.33,3,1.33,2022,Journal of Statistical Software,"Carli, F., Leonelli, M., Riccomagno, E., & Varando, G. (2022). The R Package stagedtrees for Structural Learning of Stratified Staged Trees. Journal of Statistical Software, 102, 1-30"
pilot,Discrete,Knowledge,Expert,Reverse Tree,Engineering,42,41,134,0.98,4,2.0,2,3.67,2023,Ocean Engineering,"Sakar, C., & Sokukcu, M. (2023). Dynamic analysis of pilot transfer accidents. Ocean Engineering, 287, 115823"
poultry,Discrete,Data,Data,Generic,Genetics,47,41,100,0.87,3,2.0,2,2.17,2022,BMC Bioinformatics,"Videla Rodriguez, E. A., Pertille, F., Guerrero-Bosagna, C., Mitchell, J. B., Jensen, P., & Smith, V. A. (2022). Practical application of a Bayesian network approach to poultry epigenetics and stress. BMC Bioinformatics, 23(1), 261"
project,Discrete,Knowledge,Knowledge,Generic,Environmental Science,21,21,52,1.0,3,2.0,2,2.76,2023,Environment Development and Sustainability,"Ebnerasoul, M., Ghannadpour, S. F., & Haeri, A. (2023). A collective efficacy-based approach for bi-objective sustainable project portfolio selection using interdependency network model between projects. Environment, Development and Sustainability, 25(12), 13981-14001"
projectmanagement,Discrete,Knowledge,Knowledge,Generic,Business,26,35,100,1.35,4,2.0,2,4.62,2016,International Journal of Project Management,"Qazi, A., Quigley, J., Dickson, A., & Kirytopoulos, K. (2016). Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects. International Journal of Project Management, 34(7), 1183-1198"
rainstorm,Discrete,Knowledge,Knowledge,Generic,Earth Sciences,34,33,96,0.97,3,2.0,2,3.24,2023,Natural Hazards,"Xie, X., Tian, Y., & Wei, G. (2023). Deduction of sudden rainstorm scenarios: integrating decision makers' emotions, dynamic Bayesian network and DS evidence theory. Natural Hazards, 116(3), 2935-2955."
ricci,Discrete,Data,Data,Generic,Social Sciences,6,6,13,1.0,2,2.0,2,2.0,2022,Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,"Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., & Ntoutsi, E. (2022). A survey on datasets for fairness-aware machine learning. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(3), e1452"
rockburst,Discrete,Data,Data,TAN,Engineering,6,9,53,1.5,2,2.83,3,3.0,2021,Engineering Science and Technology,"Li, X., Mao, H., Li, B., & Xu, N. (2021). Dynamic early warning of rockburst using microseismic multi-parameters based on Bayesian network. Engineering Science and Technology, an International Journal, 24(3), 715-727"
ropesegment,Discrete,Knowledge,Knowledge,Reverse Tree,Engineering,11,10,31,0.91,3,2.0,2,3.27,2024,Journal of The Institution of Engineers,"Jana, D., Kumar, D., Gupta, S., & Gupta, K. K. (2024). Availability optimization of a dragline subsystem using Bayesian network. Journal of The Institution of Engineers (India): Series D, 105(1), 77-88"
sallyclark,Discrete,Knowledge,Knowledge,Tree,Law,6,5,11,0.83,1,2.0,2,1.67,2023,Artificial Intelligence and Law,"Kowalewska, A., & Urbaniak, R. (2023). Measuring coherence with Bayesian networks. Artificial Intelligence and Law, 31(2), 369-395"
