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{"Name":"adhd","Type":"Discrete","Structure":"Fixed","Probabilities":"Data","Graph":"Naive Bayes","Area":"Psychology","Nodes":19,"Arcs":18,"Parameters":219,"Avg. Parents":0.95,"Max Parents":1,"Avg. Levels":4,"Max Levels":4,"Average Markov Blanket":1.89,"Year":2023,"Journal":"Current Psychology","Reference":"Jiang, Z., Ma, W., Flory, K., Zhang, D., Zhou, W., Shi, D., ... & Liu, R. (2023). Development of a computerized adaptive testing for ADHD using Bayesian networks: An attempt at classification. Current Psychology, 42(22), 19230-19240."}
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{"Name":"aircrash","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Law","Nodes":9,"Arcs":8,"Parameters":144,"Avg. Parents":0.89,"Max Parents":2,"Avg. Levels":3.33,"Max Levels":8,"Average Markov Blanket":2,"Year":2020,"Journal":"Forensic Science International","Reference":"Maskell, P. D., & Jackson, G. (2020). Application of a Bayesian network to aid the interpretation of blood alcohol (ethanol) concentrations in air crashes. Forensic Science International, 308, 110174"}
{"Name":"algal1","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Environmental Science","Nodes":9,"Arcs":9,"Parameters":27,"Avg. Parents":1,"Max Parents":3,"Avg. Levels":2.11,"Max Levels":3,"Average Markov Blanket":3.11,"Year":2022,"Journal":"Hydrology and Earth System Sciences","Reference":"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."}
{"Name":"algal2","Type":"Gaussian","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Environmental Science","Nodes":9,"Arcs":9,"Parameters":27,"Avg. Parents":1,"Max Parents":3,"Average Markov Blanket":3.11,"Year":2022,"Journal":"Hydrology and Earth System Sciences","Reference":"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."}
{"Name":"algalactivity1","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Environmental Science","Nodes":8,"Arcs":10,"Parameters":22,"Avg. Parents":1.25,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.25,"Year":2023,"Journal":"Water Research","Reference":"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"}
{"Name":"algalactivity2","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Environmental Science","Nodes":8,"Arcs":15,"Parameters":43,"Avg. Parents":1.88,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":5.5,"Year":2023,"Journal":"Water Research","Reference":"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"}
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{"Name":"APSsystem","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Computer Science","Nodes":10,"Arcs":9,"Parameters":21,"Avg. Parents":0.9,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.2,"Year":2022,"Journal":"Procedia Computer Science","Reference":"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."}
{"Name":"arcticwaters","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Engineering","Nodes":46,"Arcs":59,"Parameters":357,"Avg. Parents":1.28,"Max Parents":4,"Avg. Levels":2.26,"Max Levels":5,"Average Markov Blanket":4.09,"Year":2023,"Journal":"Reliability Engineering & System Safety","Reference":"Fu, S., Zhang, Y., Zhang, M., Han, B., & Wu, Z. (2023). An object-oriented Bayesian network model for the quantitative risk assessment of navigational accidents in ice-covered Arctic waters. Reliability Engineering & System Safety, 238, 109459"}
{"Name":"argument","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Psychology","Nodes":10,"Arcs":11,"Parameters":38,"Avg. Parents":1.1,"Max Parents":3,"Avg. Levels":2.2,"Max Levels":3,"Average Markov Blanket":3.4,"Year":2023,"Journal":"Thinking & Reasoning","Reference":"Pei, K. N., & Chin, C. S. A. (2023). Towards an empirically informed normative Bayesian scheme-based account of argument from expert opinion. Thinking & Reasoning, 29(4), 726-759"}
{"Name":"asia","Type":"Discrete","Structure":"Synthetic","Probabilities":"Synthetic","Graph":"Generic","Area":"Medicine","Nodes":8,"Arcs":8,"Parameters":18,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.5,"Year":1988,"Journal":"Journal of the Royal Statistical Society: Series B","Reference":"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"}
{"Name":"asia","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Medicine","Nodes":11,"Arcs":8,"Parameters":23,"Avg. Parents":0.73,"Max Parents":2,"Avg. Levels":2.09,"Max Levels":3,"Average Markov Blanket":1.64,"Year":2024,"Journal":"Computational and Structural Biotechnology Journal","Reference":"Filigheddu, M. T., Leonelli, M., Varando, G., Gomez-Bermejo, M. A., Ventura-Diaz, S., Gorospe, L., & Fortun, J. (2024). Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi. Computational and Structural Biotechnology Journal, 24, 12-22"}
{"Name":"augmenting","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Engineering","Nodes":6,"Arcs":6,"Parameters":53,"Avg. Parents":1,"Max Parents":3,"Avg. Levels":2.67,"Max Levels":3,"Average Markov Blanket":3.67,"Year":2019,"Journal":"International Symposium on Real-Time Distributed Computing","Reference":"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"}
{"Name":"bank","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Business","Nodes":4,"Arcs":5,"Parameters":19,"Avg. Parents":1.25,"Max Parents":2,"Avg. Levels":2.5,"Max Levels":4,"Average Markov Blanket":2.5,"Year":2024,"Journal":"Data Mining and Knowledge Discovery","Reference":"Leonelli, M., & Varando, G. (2024). Structural learning of simple staged trees. Data Mining and Knowledge Discovery, 38(3), 1520-1544"}
{"Name":"bankruptcy","Type":"Discrete","Structure":"Fixed","Probabilities":"Data","Graph":"Naive Bayes","Area":"Economics","Nodes":9,"Arcs":8,"Parameters":29,"Avg. Parents":0.89,"Max Parents":1,"Avg. Levels":2.67,"Max Levels":3,"Average Markov Blanket":1.78,"Year":2007,"Journal":"European Journal of Operational Research","Reference":"Sun, L., & Shenoy, P. P. (2007). Using Bayesian networks for bankruptcy prediction: Some methodological issues. European Journal of Operational Research, 180(2), 738-753"}
{"Name":"beam1","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Reverse Tree","Area":"Engineering","Nodes":6,"Arcs":5,"Parameters":55,"Avg. Parents":0.83,"Max Parents":3,"Avg. Levels":2.83,"Max Levels":5,"Average Markov Blanket":2.67,"Year":2022,"Journal":"International Civil Engineering and Architecture Conference","Reference":"Obaid, O., & Leblouba, M. (2022, March). Bayesian Networks and Their Application to the Reliability of FRP Strengthened Beams. In International Civil Engineering and Architecture Conference (pp. 277-284). Singapore: Springer Nature Singapore"}
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{"Name":"beatles","Type":"Discrete","Structure":"Fixed","Probabilities":"Knowledge","Graph":"Reverse Naive Bayes","Area":"Law","Nodes":5,"Arcs":4,"Parameters":20,"Avg. Parents":0.8,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4,"Year":2023,"Journal":"Artificial Intelligence and Law","Reference":"Kowalewska, A., & Urbaniak, R. (2023). Measuring coherence with Bayesian networks. Artificial Intelligence and Law, 31(2), 369-395"}
{"Name":"blacksea","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Engineering","Nodes":48,"Arcs":87,"Parameters":252,"Avg. Parents":1.81,"Max Parents":4,"Avg. Levels":2.06,"Max Levels":5,"Average Markov Blanket":5.42,"Year":2020,"Journal":"Risk Analysis","Reference":"Ugurlu, O., Yildiz, S., Loughney, S., Wang, J., Kuntchulia, S., & Sharabidze, I. (2020). Analyzing collision, grounding, and sinking accidents occurring in the Black Sea utilizing HFACS and Bayesian networks. Risk analysis, 40(12), 2610-2638a"}
{"Name":"blockchain","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Social Sciences","Nodes":12,"Arcs":13,"Parameters":73,"Avg. Parents":1.08,"Max Parents":5,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":5.5,"Year":2021,"Journal":"Technological Forecasting and Social Change","Reference":"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"}
{"Name":"BOPfailure1","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Engineering","Nodes":30,"Arcs":29,"Parameters":132,"Avg. Parents":0.97,"Max Parents":5,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4.27,"Year":2023,"Journal":"PLoS One","Reference":"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"}
{"Name":"BOPfailure2","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Engineering","Nodes":46,"Arcs":45,"Parameters":350,"Avg. Parents":0.98,"Max Parents":7,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":5.3,"Year":2023,"Journal":"PLoS One","Reference":"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"}
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{"Name":"building","Type":"Gaussian","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Engineering","Nodes":24,"Arcs":32,"Parameters":80,"Avg. Parents":1.33,"Max Parents":4,"Average Markov Blanket":4.33,"Year":2003,"Journal":"Reliability Engineering & System Safety","Reference":"Castillo, E., & Kjaerulff, U. (2003). Sensitivity analysis in Gaussian Bayesian networks using a symbolic-numerical technique. Reliability Engineering & System Safety, 79(2), 139-148"}
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{"Name":"burglar","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Naive Bayes","Area":"Psychology","Nodes":3,"Arcs":2,"Parameters":11,"Avg. Parents":0.67,"Max Parents":1,"Avg. Levels":2.67,"Max Levels":3,"Average Markov Blanket":1.33,"Year":2020,"Journal":"Cognitive Psychology","Reference":"Liefgreen, A., Pilditch, T., & Lagnado, D. (2020). Strategies for selecting and evaluating information. Cognitive Psychology, 123, 101332"}
{"Name":"cachexia1","Type":"Gaussian","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Genetics","Nodes":6,"Arcs":8,"Parameters":20,"Avg. Parents":1.33,"Max Parents":3,"Average Markov Blanket":3,"Year":2020,"Journal":"Journal of Machine Learning Research","Reference":"Gorgen, C., & Leonelli, M. (2020). Model-preserving sensitivity analysis for families of Gaussian distributions. Journal of Machine Learning Research, 21(84), 1-32"}
{"Name":"cachexia2","Type":"Gaussian","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Genetics","Nodes":6,"Arcs":8,"Parameters":20,"Avg. Parents":1.33,"Max Parents":3,"Average Markov Blanket":3,"Year":2020,"Journal":"Journal of Machine Learning Research","Reference":"Gorgen, C., & Leonelli, M. (2020). Model-preserving sensitivity analysis for families of Gaussian distributions. Journal of Machine Learning Research, 21(84), 1-32"}
{"Name":"cardiovascular","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Medicine","Nodes":13,"Arcs":40,"Parameters":2928,"Avg. Parents":3.08,"Max Parents":6,"Avg. Levels":2.77,"Max Levels":6,"Average Markov Blanket":8.46,"Year":2023,"Journal":"Computer Methods and Programs in Biomedicine","Reference":"Ordovas, J. M., Rios-Insua, D., Santos-Lozano, A., Lucia, A., Torres, A., Kosgodagan, A., & Camacho, J. M. (2023). A Bayesian network model for predicting cardiovascular risk. Computer Methods and Programs in Biomedicine, 231, 107405"}
{"Name":"case","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Law","Nodes":23,"Arcs":35,"Parameters":109,"Avg. Parents":1.52,"Max Parents":4,"Avg. Levels":2.04,"Max Levels":3,"Average Markov Blanket":4.26,"Year":2024,"Journal":"International Conference on Hybrid Human-Artificial Intelligence","Reference":"Van Leeuwen, L., Verbrugge, R., Verheij, B., & Renooij, S. (2024, June). Building a Stronger Case: Combining Evidence and Law in Scenario-Based Bayesian Networks. In 3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI 2024 (pp. 291-299). IOS Press"}
{"Name":"catchment","Type":"Discrete","Structure":"Mixed","Probabilities":"Data","Graph":"Generic","Area":"Environmental Science","Nodes":19,"Arcs":26,"Parameters":179,"Avg. Parents":1.37,"Max Parents":3,"Avg. Levels":2.63,"Max Levels":4,"Average Markov Blanket":4.32,"Year":2020,"Journal":"Journal of Applied Ecology","Reference":"Feld, C. K., Saeedghalati, M., & Hering, D. (2020). A framework to diagnose the causes of river ecosystem deterioration using biological symptoms. Journal of Applied Ecology, 57(11), 2271-2284"}
{"Name":"charleston","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Environmental Science","Nodes":24,"Arcs":35,"Parameters":1272,"Avg. Parents":1.46,"Max Parents":4,"Avg. Levels":3.5,"Max Levels":4,"Average Markov Blanket":5.33,"Year":2021,"Journal":"Integrated Environmental Assessment and Management","Reference":"Cains, M. G., & Henshel, D. (2021). Parameterization framework and quantification approach for integrated risk and resilience assessments. Integrated Environmental Assessment and Management, 17(1), 131-146"}
{"Name":"chds","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Tree","Area":"Medicine","Nodes":4,"Arcs":3,"Parameters":10,"Avg. Parents":0.75,"Max Parents":1,"Avg. Levels":2.25,"Max Levels":3,"Average Markov Blanket":1.5,"Year":2013,"Journal":"International Journal of Approximate Reasoning","Reference":"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"}
{"Name":"cng","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Earth Sciences","Nodes":86,"Arcs":85,"Parameters":2404,"Avg. Parents":0.99,"Max Parents":11,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":5.35,"Year":2024,"Journal":"Scientific Reports","Reference":"Abbasi Kharajou, B., Ahmadi, H., Rafiei, M., & Moradi Hanifi, S. (2024). Quantitative risk estimation of CNG station by using fuzzy bayesian networks and consequence modeling. Scientific Reports, 14(1), 4266"}
{"Name":"compaction","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Reverse Tree","Area":"Agricultural Sciences","Nodes":6,"Arcs":5,"Parameters":54,"Avg. Parents":0.83,"Max Parents":2,"Avg. Levels":3.33,"Max Levels":5,"Average Markov Blanket":2.33,"Year":2021,"Journal":"Geoderma","Reference":"Roberton, S. D., Lobsey, C. R., & Bennett, J. M. (2021). A Bayesian approach toward the use of qualitative information to inform on-farm decision making: the example of soil compaction. Geoderma, 382, 114705"}
{"Name":"conasense","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Computer Science","Nodes":4,"Arcs":6,"Parameters":107,"Avg. Parents":1.5,"Max Parents":3,"Avg. Levels":3.25,"Max Levels":4,"Average Markov Blanket":3,"Year":2022,"Journal":"International Symposium on Wireless Personal Multimedia Communications","Reference":"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"}
{"Name":"concrete1","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Chemical Engineering","Nodes":4,"Arcs":4,"Parameters":46,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":3.25,"Max Levels":5,"Average Markov Blanket":2.5,"Year":2023,"Journal":"Expert Systems With Applications","Reference":"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"}
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{"Name":"consequenceCovid","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Social Sciences","Nodes":15,"Arcs":34,"Parameters":382,"Avg. Parents":2.27,"Max Parents":4,"Avg. Levels":2.93,"Max Levels":5,"Average Markov Blanket":5.73,"Year":2024,"Journal":"arXiv","Reference":"Ballester-Ripoll, R., & Leonelli, M. (2024). Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks. arXiv preprint arXiv:2406.05764"}
{"Name":"constructionproductivity","Type":"Discrete","Structure":"Expert","Probabilities":"Expert","Graph":"Generic","Area":"Engineering","Nodes":18,"Arcs":19,"Parameters":74,"Avg. Parents":1.06,"Max Parents":5,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.89,"Year":2022,"Journal":"Engineering, Construction and Architectural Management","Reference":"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"}
{"Name":"coral1","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"TAN","Area":"Environmental Science","Nodes":8,"Arcs":12,"Parameters":154,"Avg. Parents":1.5,"Max Parents":2,"Avg. Levels":3,"Max Levels":5,"Average Markov Blanket":3,"Year":2021,"Journal":"Integrated Environmental Assessment and Management","Reference":"Carriger, J. F., Yee, S. H., & Fisher, W. S. (2021). Assessing coral reef condition indicators for local and global stressors using Bayesian networks. Integrated Environmental Assessment and Management, 17(1), 165-187"}
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{"Name":"corical","Type":"Discrete","Structure":"Expert","Probabilities":"Mixed","Graph":"Generic","Area":"Medicine","Nodes":20,"Arcs":26,"Parameters":210,"Avg. Parents":1.3,"Max Parents":4,"Avg. Levels":2.75,"Max Levels":10,"Average Markov Blanket":3.8,"Year":2021,"Journal":"Vaccine","Reference":"Lau, C. L., Mayfield, H. J., Sinclair, J. E., Brown, S. J., Waller, M., Enjeti, A. K., ... & Litt, J. (2021). Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework. Vaccine, 39(51), 7429-7440"}
{"Name":"corrosion","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Generic","Area":"Engineering","Nodes":22,"Arcs":48,"Parameters":242,"Avg. Parents":1.09,"Max Parents":4,"Avg. Levels":2.55,"Max Levels":4,"Average Markov Blanket":3.91,"Year":2023,"Journal":"Engineering Reliability & System Safety","Reference":"Dao, U., Sajid, Z., Khan, F., & Zhang, Y. (2023). Dynamic Bayesian network model to study under-deposit corrosion. Reliability Engineering & System Safety, 237, 109370"}
{"Name":"corticosteroid","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Medicine","Nodes":3,"Arcs":3,"Parameters":7,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2,"Year":2021,"Journal":"Reumathology","Reference":"Kishi, T., Warren-Hicks, W., Bayat, N., Targoff, I. N., Huber, A. M., Ward, M. M., ... & with the Childhood Myositis Heterogeneity Study Group. (2021). Corticosteroid discontinuation, complete clinical response and remission in juvenile dermatomyositis. Rheumatology, 60(5), 2134-2145"}
{"Name":"covid1","Type":"Discrete","Structure":"Fixed","Probabilities":"Data","Graph":"Naive Bayes","Area":"Medicine","Nodes":12,"Arcs":11,"Parameters":71,"Avg. Parents":0.92,"Max Parents":1,"Avg. Levels":2.33,"Max Levels":6,"Average Markov Blanket":1.83,"Year":2024,"Journal":"Scientific Reports","Reference":"Demirbaga, U., Kaur, N., & Aujla, G. S. (2024). Uncovering hidden and complex relations of pandemic dynamics using an AI driven system. Scientific Reports, 14(1), 15433"}
{"Name":"covid2","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"TAN","Area":"Medicine","Nodes":12,"Arcs":21,"Parameters":131,"Avg. Parents":1.75,"Max Parents":2,"Avg. Levels":2.33,"Max Levels":6,"Average Markov Blanket":3.5,"Year":2024,"Journal":"Scientific Reports","Reference":"Demirbaga, U., Kaur, N., & Aujla, G. S. (2024). Uncovering hidden and complex relations of pandemic dynamics using an AI driven system. Scientific Reports, 14(1), 15433"}
{"Name":"covid3","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Medicine","Nodes":12,"Arcs":30,"Parameters":603,"Avg. Parents":2.5,"Max Parents":9,"Avg. Levels":2.33,"Max Levels":6,"Average Markov Blanket":9,"Year":2024,"Journal":"Scientific Reports","Reference":"Demirbaga, U., Kaur, N., & Aujla, G. S. (2024). Uncovering hidden and complex relations of pandemic dynamics using an AI driven system. Scientific Reports, 14(1), 15433"}
{"Name":"covidfear","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Psychology","Nodes":9,"Arcs":6,"Parameters":40,"Avg. Parents":0.67,"Max Parents":1,"Avg. Levels":2.78,"Max Levels":3,"Average Markov Blanket":1.33,"Year":2024,"Journal":"Applied Intelligence","Reference":"Leonelli, M., & Varando, G. (2024). Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear. Applied Intelligence, 54(2), 1734-1750"}
{"Name":"covidrisk","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Social Sciences","Nodes":9,"Arcs":13,"Parameters":27,"Avg. Parents":1.44,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.11,"Year":2022,"Journal":"International Conference on Probabilistic Graphical Models","Reference":"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"}
{"Name":"covidtech","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Psychology","Nodes":18,"Arcs":18,"Parameters":93,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2.72,"Max Levels":4,"Average Markov Blanket":2.11,"Year":2023,"Journal":"International Journal of Computer Science","Reference":"Ballester-Ripoll, R., & Leonelli, M. (2023). The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. International Journal of Approximate Reasoning, 159, 108929"}
{"Name":"covidtest","Type":"Hybrid","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Medicine","Nodes":15,"Arcs":25,"Parameters":68,"Avg. Parents":1.67,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4.93,"Year":2021,"Journal":"International Conference on Machine Learning and Applications","Reference":"Peled, A., & Fine, S. (2021). Discrete Latent Variables Discovery and Structure Learning in Mixed Bayesian Networks. In 20th IEEE International Conference on Machine Learning and Applications (pp. 248-255). IEEE"}
{"Name":"crimescene","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Law","Nodes":9,"Arcs":13,"Parameters":45,"Avg. Parents":1.44,"Max Parents":3,"Avg. Levels":2.22,"Max Levels":4,"Average Markov Blanket":4.22,"Year":2020,"Journal":"Australian Journal of Forensic Sciences","Reference":"Mayuoni-Kirshenbaum, L., Waiskopf, O., Finkelstein, N., & Pasternak, Z. (2022). How did the DNA of a suspect get to the crime scene? A practical study in DNA transfer during lock-picking. Australian Journal of Forensic Sciences, 54(1), 15-25"}
{"Name":"criminal1","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Law","Nodes":12,"Arcs":14,"Parameters":50,"Avg. Parents":1.17,"Max Parents":3,"Avg. Levels":2.17,"Max Levels":4,"Average Markov Blanket":3.17,"Year":2022,"Journal":"International Conference on Artificial Intelligence and Law","Reference":"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)"}
{"Name":"criminal2","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Law","Nodes":12,"Arcs":14,"Parameters":50,"Avg. Parents":1.17,"Max Parents":3,"Avg. Levels":2.17,"Max Levels":4,"Average Markov Blanket":3.17,"Year":2022,"Journal":"International Conference on Artificial Intelligence and Law","Reference":"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)"}
{"Name":"criminal3","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Law","Nodes":8,"Arcs":9,"Parameters":19,"Avg. Parents":1.12,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.5,"Year":2022,"Journal":"International Conference on Artificial Intelligence and Law","Reference":"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)"}
{"Name":"criminal4","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Law","Nodes":8,"Arcs":9,"Parameters":19,"Avg. Parents":1.12,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.5,"Year":2022,"Journal":"International Conference on Artificial Intelligence and Law","Reference":"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)"}
{"Name":"curacao1","Type":"Discrete","Structure":"Expert","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Environmental Science","Nodes":13,"Arcs":12,"Parameters":77,"Avg. Parents":0.92,"Max Parents":4,"Avg. Levels":2.38,"Max Levels":3,"Average Markov Blanket":4,"Year":2024,"Journal":"Environmental Science and Policy","Reference":"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"}
{"Name":"curacao2","Type":"Discrete","Structure":"Expert","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Environmental Science","Nodes":13,"Arcs":12,"Parameters":114,"Avg. Parents":0.92,"Max Parents":5,"Avg. Levels":2.38,"Max Levels":3,"Average Markov Blanket":4.15,"Year":2024,"Journal":"Environmental Science and Policy","Reference":"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"}
{"Name":"curacao3","Type":"Discrete","Structure":"Expert","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Environmental Science","Nodes":19,"Arcs":18,"Parameters":84,"Avg. Parents":0.95,"Max Parents":4,"Avg. Levels":2.21,"Max Levels":3,"Average Markov Blanket":4,"Year":2024,"Journal":"Environmental Science and Policy","Reference":"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"}
{"Name":"curacao4","Type":"Discrete","Structure":"Expert","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Environmental Science","Nodes":13,"Arcs":12,"Parameters":60,"Avg. Parents":0.92,"Max Parents":4,"Avg. Levels":2.46,"Max Levels":3,"Average Markov Blanket":3.38,"Year":2024,"Journal":"Environmental Science and Policy","Reference":"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"}
{"Name":"curacao5","Type":"Discrete","Structure":"Expert","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Environmental Science","Nodes":13,"Arcs":12,"Parameters":60,"Avg. Parents":0.92,"Max Parents":4,"Avg. Levels":2.46,"Max Levels":3,"Average Markov Blanket":3.38,"Year":2024,"Journal":"Environmental Science and Policy","Reference":"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"}
{"Name":"darktriad","Type":"Hybrid","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Psychology","Nodes":14,"Arcs":15,"Parameters":44,"Avg. Parents":1.07,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.71,"Year":2024,"Journal":"Personality and Individual Differences","Reference":"Zaitsu, W. (2024). Bayesian Network modeling for Dark Triad, aggression, and empathy. Personality and Individual Differences, 230, 112805"}
{"Name":"diabetes","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Medicine","Nodes":9,"Arcs":8,"Parameters":18,"Avg. Parents":0.89,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2,"Year":2023,"Journal":"Knowledge-Based Systems","Reference":"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"}
{"Name":"diagnosis","Type":"Gaussian","Structure":"Synthetic","Probabilities":"Synthetic","Graph":"Generic","Area":"Engineering","Nodes":16,"Arcs":15,"Parameters":47,"Avg. Parents":0.94,"Max Parents":2,"Average Markov Blanket":2.12,"Year":2022,"Journal":"Control Engineering Practice","Reference":"Yang, W. T., Reis, M. S., Borodin, V., Juge, M., & Roussy, A. (2022). An interpretable unsupervised Bayesian network model for fault detection and diagnosis. Control Engineering Practice, 127, 105304"}
{"Name":"dioxins","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"TAN","Area":"Agricultural Sciences","Nodes":9,"Arcs":15,"Parameters":1929,"Avg. Parents":1.67,"Max Parents":2,"Avg. Levels":9,"Max Levels":27,"Average Markov Blanket":3.33,"Year":2023,"Journal":"Risk Analysis","Reference":"Wang, Z., van der Fels-Klerx, H. J., & Oude Lansink, A. G. J. M. (2023). Designing optimal food safety monitoring schemes using Bayesian network and integer programming: The case of monitoring dioxins and DL-PCBs. Risk Analysis, 43(7), 1400-1413"}
{"Name":"disputed1","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Law","Nodes":11,"Arcs":11,"Parameters":25,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.55,"Year":2024,"Journal":"Forensic Science International: Synergy","Reference":"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"}
{"Name":"disputed2","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Law","Nodes":17,"Arcs":19,"Parameters":55,"Avg. Parents":1.12,"Max Parents":2,"Avg. Levels":2.12,"Max Levels":4,"Average Markov Blanket":3.06,"Year":2024,"Journal":"Forensic Science International: Synergy","Reference":"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"}
{"Name":"disputed3","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Law","Nodes":27,"Arcs":34,"Parameters":96,"Avg. Parents":1.26,"Max Parents":3,"Avg. Levels":2.15,"Max Levels":4,"Average Markov Blanket":3.41,"Year":2024,"Journal":"Forensic Science International: Synergy","Reference":"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"}
{"Name":"disputed4","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Law","Nodes":23,"Arcs":29,"Parameters":80,"Avg. Parents":1.26,"Max Parents":3,"Avg. Levels":2.17,"Max Levels":4,"Average Markov Blanket":3.3,"Year":2024,"Journal":"Forensic Science International: Synergy","Reference":"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"}
{"Name":"dragline","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Reverse Tree","Area":"Engineering","Nodes":51,"Arcs":50,"Parameters":876,"Avg. Parents":0.98,"Max Parents":9,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":6.43,"Year":2023,"Journal":"Mining, Metallurgy & Exploration","Reference":"Kumar, D., Jana, D., Gupta, S., & Yadav, P. K. (2023). Bayesian network approach for dragline reliability analysis: A case study. Mining, Metallurgy & Exploration, 40(1), 347-365"}
{"Name":"drainage","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Engineering","Nodes":11,"Arcs":10,"Parameters":31,"Avg. Parents":0.91,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.27,"Year":2024,"Journal":"IEEE Sensors","Reference":"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"}
{"Name":"dustexplosion","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Generic","Area":"Engineering","Nodes":26,"Arcs":32,"Parameters":142,"Avg. Parents":1.23,"Max Parents":3,"Avg. Levels":2.5,"Max Levels":5,"Average Markov Blanket":3.23,"Year":2023,"Journal":"Journal of Loss Prevention in the Process Industries","Reference":"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"}
{"Name":"earthquake","Type":"Discrete","Structure":"Expert","Probabilities":"Expert","Graph":"Generic","Area":"Engineering","Nodes":40,"Arcs":77,"Parameters":44502,"Avg. Parents":1.93,"Max Parents":5,"Avg. Levels":4.1,"Max Levels":6,"Average Markov Blanket":6.65,"Year":2021,"Journal":"Engineering Reliability & System Safety","Reference":"Goerlandt, F., & Islam, S. (2021). A Bayesian Network risk model for estimating coastal maritime transportation delays following an earthquake in British Columbia. Reliability Engineering & System Safety, 214, 107708"}
{"Name":"ecosystem","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Reverse Tree","Area":"Environmental Science","Nodes":13,"Arcs":12,"Parameters":98,"Avg. Parents":0.92,"Max Parents":3,"Avg. Levels":2.69,"Max Levels":3,"Average Markov Blanket":2.92,"Year":2022,"Journal":"Ecological Indicators","Reference":"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"}
{"Name":"electricvehicle","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Engineering","Nodes":23,"Arcs":22,"Parameters":79,"Avg. Parents":0.96,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.83,"Year":2022,"Journal":"Mathematics","Reference":"Chen, J., Li, K., & Yang, S. (2022). Electric vehicle fire risk assessment based on WBS-RBS and fuzzy BN coupling. Mathematics, 10(20), 3799"}
{"Name":"electrolysis","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Chemical Engineering","Nodes":16,"Arcs":18,"Parameters":83,"Avg. Parents":1.12,"Max Parents":5,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4.88,"Year":2023,"Journal":"Journal of Environmental Chemical Engineering","Reference":"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"}
{"Name":"emergency","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Engineering","Nodes":17,"Arcs":16,"Parameters":36,"Avg. Parents":0.94,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.24,"Year":2024,"Journal":"Reliability Engineering & System Safety","Reference":"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"}
{"Name":"engines","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Generic","Area":"Engineering","Nodes":12,"Arcs":18,"Parameters":80,"Avg. Parents":1.5,"Max Parents":5,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":6.17,"Year":2024,"Journal":"Ships and Offshore Structures","Reference":"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."}
{"Name":"enrollment","Type":"Discrete","Structure":"Fixed","Probabilities":"Data","Graph":"Naive Bayes","Area":"Social Sciences","Nodes":8,"Arcs":7,"Parameters":33,"Avg. Parents":0.88,"Max Parents":1,"Avg. Levels":3.12,"Max Levels":5,"Average Markov Blanket":1.75,"Year":2024,"Journal":"Sustainability","Reference":"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"}
{"Name":"estuary","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Environmental Science","Nodes":30,"Arcs":44,"Parameters":86202,"Avg. Parents":1.47,"Max Parents":7,"Avg. Levels":4.9,"Max Levels":11,"Average Markov Blanket":5.4,"Year":2022,"Journal":"Frontiers in Marine Science","Reference":"Zeldis, J. R., & Plew, D. R. (2022). Predicting and scoring estuary ecological health using a Bayesian belief network. Frontiers in Marine Science, 9, 898992"}
{"Name":"expenditure","Type":"Gaussian","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Economics","Nodes":12,"Arcs":20,"Parameters":44,"Avg. Parents":1.67,"Max Parents":3,"Average Markov Blanket":4.33,"Year":2022,"Journal":"Mathematics","Reference":"Tsagris, M. (2022). The FEDHC Bayesian network learning algorithm. Mathematics, 10(15), 2604"}
{"Name":"fingermarks1","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Law","Nodes":12,"Arcs":20,"Parameters":45,"Avg. Parents":1.67,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.83,"Year":2021,"Journal":"Forensic Science International","Reference":"de Ronde, A., Kokshoorn, B., de Puit, M., & de Poot, C. J. (2021). Using case specific experiments to evaluate fingermarks on knives given activity level propositions. Forensic Science International, 320, 110710"}
{"Name":"fingermarks2","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Law","Nodes":12,"Arcs":20,"Parameters":4337,"Avg. Parents":1.67,"Max Parents":3,"Avg. Levels":7,"Max Levels":9,"Average Markov Blanket":3.83,"Year":2021,"Journal":"Forensic Science International","Reference":"de Ronde, A., Kokshoorn, B., de Puit, M., & de Poot, C. J. (2021). Using case specific experiments to evaluate fingermarks on knives given activity level propositions. Forensic Science International, 320, 110710"}
{"Name":"fire","Type":"Discrete","Structure":"Expert","Probabilities":"Expert","Graph":"Generic","Area":"Psychology","Nodes":11,"Arcs":14,"Parameters":43,"Avg. Parents":1.27,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4.36,"Year":2021,"Journal":"Fire Technology","Reference":"Ramli, N., Ghani, N. A., Ahmad, N., & Hashim, I. H. M. (2021). Psychological response in fire: a fuzzy Bayesian network approach using expert judgment. Fire Technology, 57, 2305-2338"}
{"Name":"firealarm","Type":"Discrete","Structure":"Synthetic","Probabilities":"Synthetic","Graph":"Generic","Area":"Engineering","Nodes":6,"Arcs":5,"Parameters":17,"Avg. Parents":0.83,"Max Parents":2,"Avg. Levels":2.17,"Max Levels":3,"Average Markov Blanket":2,"Year":2002,"Journal":"Journal of Artificial Intelligence Research","Reference":"Hei Chan, Adnan Darwiche (2002). When do numbers really matter?. Journal of Artificial Intelligence Research 17 (265-287)"}
{"Name":"firerisk","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Engineering","Nodes":23,"Arcs":26,"Parameters":57,"Avg. Parents":1.13,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.87,"Year":2024,"Journal":"Journal of Theoretical and Applied Information Technology","Reference":"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)"}
{"Name":"flood","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Agricultural Sciences","Nodes":22,"Arcs":42,"Parameters":1828,"Avg. Parents":1.91,"Max Parents":4,"Avg. Levels":3.5,"Max Levels":7,"Average Markov Blanket":5.64,"Year":2023,"Journal":"Smart Agricultural Technology","Reference":"Ali, Q. (2023). A trade-off between farm production and flood alleviation using land use tillage preferences as a natural flood management (NFM) strategy. Smart Agricultural Technology, 6, 100361"}
{"Name":"fluids1","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Law","Nodes":4,"Arcs":4,"Parameters":72,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":3,"Max Levels":8,"Average Markov Blanket":2.5,"Year":2022,"Journal":"Forensic Science International","Reference":"Samie, L., Champod, C., Delemont, S., Basset, P., Hicks, T., & Castella, V. (2022). Use of Bayesian Networks for the investigation of the nature of biological material in casework. Forensic Science International, 331, 111174"}
{"Name":"fluids2","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Reverse Tree","Area":"Law","Nodes":5,"Arcs":4,"Parameters":65,"Avg. Parents":0.8,"Max Parents":2,"Avg. Levels":3.6,"Max Levels":8,"Average Markov Blanket":2.4,"Year":2022,"Journal":"Forensic Science International","Reference":"Samie, L., Champod, C., Delemont, S., Basset, P., Hicks, T., & Castella, V. (2022). Use of Bayesian Networks for the investigation of the nature of biological material in casework. Forensic Science International, 331, 111174"}
{"Name":"fluids3","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Law","Nodes":9,"Arcs":11,"Parameters":638,"Avg. Parents":1.22,"Max Parents":3,"Avg. Levels":4.44,"Max Levels":8,"Average Markov Blanket":3.78,"Year":2022,"Journal":"Forensic Science International","Reference":"Samie, L., Champod, C., Delemont, S., Basset, P., Hicks, T., & Castella, V. (2022). Use of Bayesian Networks for the investigation of the nature of biological material in casework. Forensic Science International, 331, 111174"}
{"Name":"foodsecurity","Type":"Gaussian","Structure":"Synthetic","Probabilities":"Synthetic","Graph":"Generic","Area":"Social Sciences","Nodes":4,"Arcs":4,"Parameters":12,"Avg. Parents":1,"Max Parents":2,"Average Markov Blanket":2,"Year":2020,"Journal":"OR Spectrum","Reference":"Leonelli, M., Riccomagno, E., & Smith, J. Q. (2020). Coherent combination of probabilistic outputs for group decision making: an algebraic approach. OR Spectrum, 42(2), 499-528"}
{"Name":"foodallergy1","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Medicine","Nodes":8,"Arcs":12,"Parameters":31,"Avg. Parents":1.5,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.75,"Year":2023,"Journal":"World Allergy Organization Journal","Reference":"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"}
{"Name":"foodallergy2","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Medicine","Nodes":8,"Arcs":8,"Parameters":19,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.25,"Year":2023,"Journal":"World Allergy Organization Journal","Reference":"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"}
{"Name":"foodallergy3","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Medicine","Nodes":8,"Arcs":8,"Parameters":17,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.25,"Year":2023,"Journal":"World Allergy Organization Journal","Reference":"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"}
{"Name":"fundraising","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Business","Nodes":8,"Arcs":8,"Parameters":25,"Avg. Parents":1,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.5,"Year":2022,"Journal":"Mathematics","Reference":"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."}
{"Name":"gasexplosion","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Chemical Engineering","Nodes":18,"Arcs":23,"Parameters":57,"Avg. Parents":1.28,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.78,"Year":2023,"Journal":"Processes","Reference":"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"}
{"Name":"gasifier","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Chemical Engineering","Nodes":40,"Arcs":39,"Parameters":159,"Avg. Parents":0.98,"Max Parents":5,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4,"Year":2022,"Journal":"Processes","Reference":"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"}
{"Name":"GDIpathway1","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Genetics","Nodes":28,"Arcs":31,"Parameters":128,"Avg. Parents":1.11,"Max Parents":6,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4,"Year":2022,"Journal":"IEEE Journal of Biomedical and Health Informatics","Reference":"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"}
{"Name":"GDIpathway2","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Genetics","Nodes":28,"Arcs":31,"Parameters":128,"Avg. Parents":1.11,"Max Parents":6,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4,"Year":2022,"Journal":"IEEE Journal of Biomedical and Health Informatics","Reference":"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"}
{"Name":"gonorrhoeae","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Reverse Tree","Area":"Medicine","Nodes":10,"Arcs":9,"Parameters":47,"Avg. Parents":0.9,"Max Parents":3,"Avg. Levels":2.3,"Max Levels":4,"Average Markov Blanket":3.2,"Year":2024,"Journal":"BMC Infectious Diseases","Reference":"Do, P. C., Assefa, Y. A., Batikawai, S. M., Abate, M. A., & Reid, S. A. (2024). Policy, practice, and prediction: model-based approaches to evaluating N. gonorrhoeae antibiotic susceptibility test uptake in Australia. BMC Infectious Diseases, 24(1), 498"}
{"Name":"greencredit","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Energy","Nodes":10,"Arcs":12,"Parameters":92,"Avg. Parents":1.2,"Max Parents":2,"Avg. Levels":3,"Max Levels":3,"Average Markov Blanket":2.8,"Year":2023,"Journal":"Journal of Systems Science and Complexity","Reference":"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"}
{"Name":"grounding","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Engineering","Nodes":36,"Arcs":45,"Parameters":207,"Avg. Parents":1.25,"Max Parents":6,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4.89,"Year":2022,"Journal":"Reliability Engineering & System Safety","Reference":"Fu, S., Yu, Y., Chen, J., Xi, Y., & Zhang, M. (2022). A framework for quantitative analysis of the causation of grounding accidents in arctic shipping. Reliability Engineering & System Safety, 226, 108706"}
{"Name":"healthinsurance","Type":"Hybrid","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Economics","Nodes":7,"Arcs":6,"Parameters":39,"Avg. Parents":0.86,"Max Parents":3,"Avg. Levels":3.5,"Max Levels":6,"Average Markov Blanket":2.57,"Year":2021,"Journal":"International Conference on Machine Learning and Applications","Reference":"Peled, A., & Fine, S. (2021). Discrete Latent Variables Discovery and Structure Learning in Mixed Bayesian Networks. In 20th IEEE International Conference on Machine Learning and Applications (pp. 248-255). IEEE"}
{"Name":"humanitarian","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Social Sciences","Nodes":21,"Arcs":20,"Parameters":122,"Avg. Parents":0.95,"Max Parents":1,"Avg. Levels":3,"Max Levels":3,"Average Markov Blanket":1.9,"Year":2022,"Journal":"International Conference on Probabilistic Graphical Models","Reference":"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"}
{"Name":"hydraulicsystem","Type":"Discrete","Structure":"Fixed","Probabilities":"Knowledge","Graph":"Reverse Naive Bayes","Area":"Engineering","Nodes":4,"Arcs":3,"Parameters":11,"Avg. Parents":0.75,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3,"Year":2023,"Journal":"Measurement Science and Technology","Reference":"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"}
{"Name":"income","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Economics","Nodes":13,"Arcs":20,"Parameters":407,"Avg. Parents":1.54,"Max Parents":4,"Avg. Levels":3.62,"Max Levels":9,"Average Markov Blanket":4.15,"Year":2022,"Journal":"Mathematics","Reference":"Tsagris, M. (2022). The FEDHC Bayesian network learning algorithm. Mathematics, 10(15), 2604"}
{"Name":"intensification","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Agricultural Sciences","Nodes":16,"Arcs":29,"Parameters":12243,"Avg. Parents":1.81,"Max Parents":6,"Avg. Levels":3.75,"Max Levels":7,"Average Markov Blanket":6.12,"Year":2023,"Journal":"Agricultural Economics","Reference":"Gebrekidan, B. H., Heckelei, T., & Rasch, S. (2023). Modeling intensification decisions in the Kilombero Valley floodplain: A Bayesian belief network approach. Agricultural Economics, 54(1), 23-43"}
{"Name":"intentionalattacks","Type":"Discrete","Structure":"Synthetic","Probabilities":"Synthetic","Graph":"Generic","Area":"Computer Science","Nodes":8,"Arcs":7,"Parameters":26,"Avg. Parents":0.88,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.25,"Year":2023,"Journal":"Journal of Information Security and Applications","Reference":"Chockalingam, S., Pieters, W., Teixeira, A. M., & van Gelder, P. (2023). Probability elicitation for Bayesian networks to distinguish between intentional attacks and accidental technical failures. Journal of Information Security and Applications, 75, 103497"}
{"Name":"inverters","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Engineering","Nodes":29,"Arcs":33,"Parameters":109,"Avg. Parents":1.14,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4.14,"Year":2019,"Journal":"Mathematical Problems in Engineering","Reference":"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."}
{"Name":"knowledge","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Social Sciences","Nodes":6,"Arcs":5,"Parameters":12,"Avg. Parents":0.83,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2,"Year":2020,"Journal":"Mathematical Problems in Engineering","Reference":"Wang, D., AmrilJaharadak, A., & Xiao, Y. (2020). Dynamic knowledge inference based on bayesian network learning. Mathematical Problems in Engineering, 2020(1), 6613896"}
{"Name":"kosterhavet","Type":"Discrete","Structure":"Expert","Probabilities":"Mixed","Graph":"Generic","Area":"Earth Sciences","Nodes":38,"Arcs":63,"Parameters":887,"Avg. Parents":1.66,"Max Parents":4,"Avg. Levels":2.82,"Max Levels":4,"Average Markov Blanket":5.37,"Year":2023,"Journal":"Estuarine, Coastal and Shelf Science","Reference":"Rettig, K., Hansen, A. S., Obst, M., Hering, D., & Feld, C. K. (2023). A Bayesian network to inform the management of key species in Kosterhavet National Park under contrasting storylines of environmental change. Estuarine, Coastal and Shelf Science, 280, 108158"}
{"Name":"lawschool","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Social Sciences","Nodes":10,"Arcs":21,"Parameters":117,"Avg. Parents":2.1,"Max Parents":4,"Avg. Levels":2.7,"Max Levels":6,"Average Markov Blanket":4.4,"Year":2022,"Journal":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery","Reference":"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"}
{"Name":"lexical","Type":"Gaussian","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Social Sciences","Nodes":8,"Arcs":14,"Parameters":30,"Avg. Parents":1.75,"Max Parents":4,"Average Markov Blanket":4.5,"Year":2022,"Journal":"Linguistics Vanguard","Reference":"Baumann, A., & Sekanina, K. (2022). Accounting for the relationship between lexical prevalence and acquisition with Bayesian networks and population dynamics. Linguistics Vanguard, 8(1), 209-224"}
{"Name":"lidar","Type":"Discrete","Structure":"Synthetic","Probabilities":"Synthetic","Graph":"Generic","Area":"Earth Sciences","Nodes":9,"Arcs":8,"Parameters":18,"Avg. Parents":0.89,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2,"Year":2022,"Journal":"Geospatial Informatics","Reference":"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"}
{"Name":"liquefaction","Type":"Gaussian","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Earth Sciences","Nodes":10,"Arcs":15,"Parameters":35,"Avg. Parents":1.5,"Max Parents":8,"Average Markov Blanket":7.8,"Year":2023,"Journal":"Earthquake Engineering & Structural Dynamics","Reference":"Hu, J., Xiong, B., Zhang, Z., & Wang, J. (2023). A continuous Bayesian network regression model for estimating seismic liquefaction-induced settlement of the free-field ground. Earthquake Engineering & Structural Dynamics, 52(11), 3216-3237"}
{"Name":"liquidity","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Economics","Nodes":10,"Arcs":13,"Parameters":29,"Avg. Parents":1.3,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3,"Year":2018,"Journal":"Neurocomputing","Reference":"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"}
{"Name":"lithium","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Energy","Nodes":45,"Arcs":44,"Parameters":199,"Avg. Parents":0.98,"Max Parents":5,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4.44,"Year":2023,"Journal":"Sustainability","Reference":"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"}
{"Name":"macrophytes","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Environmental Science","Nodes":15,"Arcs":20,"Parameters":174,"Avg. Parents":1.33,"Max Parents":3,"Avg. Levels":3.13,"Max Levels":9,"Average Markov Blanket":3.6,"Year":2021,"Journal":"Science of the Total Environment","Reference":"Thiemer, K., Schneider, S. C., & Demars, B. O. (2021). Mechanical removal of macrophytes in freshwater ecosystems: Implications for ecosystem structure and function. Science of the Total Environment, 782, 146671"}
{"Name":"medicaltest","Type":"Discrete","Structure":"Synthetic","Probabilities":"Synthetic","Graph":"Reverse Naive Bayes","Area":"Medicine","Nodes":3,"Arcs":2,"Parameters":6,"Avg. Parents":0.67,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2,"Year":2024,"Journal":"arXiv","Reference":"Ballester-Ripoll, R., & Leonelli, M. (2024). Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks. arXiv preprint arXiv:2406.05764"}
{"Name":"megacities","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Reverse Tree","Area":"Energy","Nodes":18,"Arcs":17,"Parameters":55,"Avg. Parents":0.94,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.44,"Year":2023,"Journal":"Journal of Cleaner Production","Reference":"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"}
{"Name":"metal","Type":"Discrete","Structure":"Fixed","Probabilities":"Expert","Graph":"Reverse Naive Bayes","Area":"Engineering","Nodes":8,"Arcs":7,"Parameters":263,"Avg. Parents":0.88,"Max Parents":7,"Avg. Levels":2.12,"Max Levels":3,"Average Markov Blanket":7,"Year":2022,"Journal":"Mindanao Journal of Science and Technology","Reference":"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)"}
{"Name":"moodstate","Type":"Discrete","Structure":"Fixed","Probabilities":"Mixed","Graph":"Naive Bayes","Area":"Psychology","Nodes":7,"Arcs":6,"Parameters":23,"Avg. Parents":0.86,"Max Parents":1,"Avg. Levels":2.71,"Max Levels":7,"Average Markov Blanket":1.71,"Year":2021,"Journal":"IEEE Access","Reference":"Tai, C. H., Chung, K. H., Teng, Y. W., Shu, F. M., & Chang, Y. S. (2021). Inference of mood state indices by using a multimodal high-level information fusion technique. IEEE Access, 9, 61256-61268"}
{"Name":"mountaingoat","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"K-Dep","Area":"Environmental Science","Nodes":7,"Arcs":12,"Parameters":289,"Avg. Parents":1.71,"Max Parents":3,"Avg. Levels":4,"Max Levels":5,"Average Markov Blanket":3.43,"Year":2022,"Journal":"Frontiers in Environmental Science","Reference":"Wilson, S. F., Nietvelt, C., Taylor, S., & Guertin, D. A. (2022). Using Bayesian networks to map winter habitat for mountain goats in coastal British Columbia, Canada. Frontiers in Environmental Science, 10, 958596"}
{"Name":"nanomaterials1","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Tree","Area":"Pharmacology","Nodes":49,"Arcs":48,"Parameters":416,"Avg. Parents":0.98,"Max Parents":1,"Avg. Levels":3.24,"Max Levels":6,"Average Markov Blanket":1.96,"Year":2020,"Journal":"Nanotoxicology","Reference":"Schmidt, J. R. A., Nogueira, D. J., Nassar, S. M., Vaz, V. P., da Silva, M. L. N., Vicentini, D. S., & Matias, W. G. (2020). Probabilistic model for assessing occupational risk during the handling of nanomaterials. Nanotoxicology, 14(9), 1258-1270"}
{"Name":"nanomaterials2","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Tree","Area":"Pharmacology","Nodes":46,"Arcs":45,"Parameters":366,"Avg. Parents":0.98,"Max Parents":1,"Avg. Levels":3.17,"Max Levels":6,"Average Markov Blanket":1.96,"Year":2020,"Journal":"Nanotoxicology","Reference":"Schmidt, J. R. A., Nogueira, D. J., Nassar, S. M., Vaz, V. P., da Silva, M. L. N., Vicentini, D. S., & Matias, W. G. (2020). Probabilistic model for assessing occupational risk during the handling of nanomaterials. Nanotoxicology, 14(9), 1258-1270"}
{"Name":"navigation","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Reverse Tree","Area":"Engineering","Nodes":4,"Arcs":3,"Parameters":28,"Avg. Parents":0.75,"Max Parents":2,"Avg. Levels":3,"Max Levels":3,"Average Markov Blanket":2,"Year":2023,"Journal":"The Journal of Navigation","Reference":"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"}
{"Name":"nuclearwaste","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Engineering","Nodes":10,"Arcs":11,"Parameters":592,"Avg. Parents":1.1,"Max Parents":8,"Avg. Levels":2.2,"Max Levels":3,"Average Markov Blanket":7.6,"Year":2019,"Journal":"Reliability Engineering & System Safety","Reference":"Tosoni, E., Salo, A., Govaerts, J., & Zio, E. (2019). Comprehensiveness of scenarios in the safety assessment of nuclear waste repositories. Reliability Engineering & System Safety, 188, 561-573"}
{"Name":"nuisancegrowth","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Environmental Science","Nodes":19,"Arcs":24,"Parameters":466,"Avg. Parents":1.26,"Max Parents":4,"Avg. Levels":3.05,"Max Levels":6,"Average Markov Blanket":3.89,"Year":2023,"Journal":"Environmental Management","Reference":"Thiemer, K., Immerzeel, B., Schneider, S., Sebola, K., Coetzee, J., Baldo, M., ... & Vermaat, J. E. (2023). Drivers of perceived nuisance growth by aquatic plants. Environmental Management, 71(5), 1024-1036"}
{"Name":"oildepot","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Engineering","Nodes":41,"Arcs":40,"Parameters":137,"Avg. Parents":0.98,"Max Parents":5,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.66,"Year":2023,"Journal":"Process Safety and Environmental Protection","Reference":"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"}
{"Name":"onlinerisk","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Engineering","Nodes":84,"Arcs":109,"Parameters":622,"Avg. Parents":1.3,"Max Parents":5,"Avg. Levels":2.29,"Max Levels":3,"Average Markov Blanket":4.4,"Year":2023,"Journal":"Ocean Engineering","Reference":"Yang, R., Bremnes, J. E., & Utne, I. B. (2023). Online risk modeling of autonomous marine systems: case study of autonomous operations under sea ice. Ocean Engineering, 281, 114765"}
{"Name":"orbital","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Tree","Area":"Earth Sciences","Nodes":60,"Arcs":59,"Parameters":271,"Avg. Parents":0.98,"Max Parents":1,"Avg. Levels":2.33,"Max Levels":4,"Average Markov Blanket":1.97,"Year":2023,"Journal":"Transactions of Nanjing University of Aeronautics and Astronautics","Reference":"Shibo, C. H. E. N., Jun, L. I., Yaen, X. I. E., Xiande, W. U., Shuhang, L. E. N. G., & Ruochu, Y. A. N. G. (2023). Approaching Intention Prediction of Orbital Maneuver Based on Dynamic Bayesian Network. Transactions of Nanjing University of Aeronautics & Astronautics, 40(4)"}
{"Name":"oxygen","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Generic","Area":"Engineering","Nodes":31,"Arcs":32,"Parameters":93,"Avg. Parents":1.03,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.55,"Year":2023,"Journal":"Heliyon","Reference":"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)"}
{"Name":"parkinson","Type":"Gaussian","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Genetics","Nodes":34,"Arcs":162,"Parameters":226,"Avg. Parents":4.76,"Max Parents":25,"Average Markov Blanket":22.59,"Year":2023,"Journal":"PLoS Computational Biology","Reference":"Raschka, T., Sood, M., Schultz, B., Altay, A., Ebeling, C., & Frohlich, H. (2023). AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease. PLOS Computational Biology, 19(2), e1009894"}
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{"Name":"permaBN","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Environmental Science","Nodes":14,"Arcs":27,"Parameters":1070,"Avg. Parents":1.93,"Max Parents":5,"Avg. Levels":3.07,"Max Levels":4,"Average Markov Blanket":6.86,"Year":2022,"Journal":"Ecological Informatics","Reference":"Beall, K., Loisel, J., & Medina-Cetina, Z. (2022). PermaBN: A Bayesian Network framework to help predict permafrost thaw in the Arctic. Ecological Informatics, 69, 101601"}
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{"Name":"pilot","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Engineering","Nodes":42,"Arcs":41,"Parameters":134,"Avg. Parents":0.98,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.67,"Year":2023,"Journal":"Ocean Engineering","Reference":"Sakar, C., & Sokukcu, M. (2023). Dynamic analysis of pilot transfer accidents. Ocean Engineering, 287, 115823"}
{"Name":"pneumonia","Type":"Discrete","Structure":"Expert","Probabilities":"Mixed","Graph":"Generic","Area":"Medicine","Nodes":62,"Arcs":171,"Parameters":1589,"Avg. Parents":2.76,"Max Parents":7,"Avg. Levels":2.24,"Max Levels":4,"Average Markov Blanket":7.55,"Year":2023,"Journal":"PLoS Computational Biology","Reference":"Wu, Y., Mascaro, S., Bhuiyan, M., Fathima, P., Mace, A. O., Nicol, M. P., ... & Blyth, C. C. (2023). Predicting the causative pathogen among children with pneumonia using a causal Bayesian network. PLoS Computational Biology, 19(3), e1010967"}
{"Name":"polymorphic","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Reverse Tree","Area":"Engineering","Nodes":22,"Arcs":21,"Parameters":2248,"Avg. Parents":0.95,"Max Parents":6,"Avg. Levels":3,"Max Levels":3,"Average Markov Blanket":5.27,"Year":2023,"Journal":"Mathematics","Reference":"Zheng, W., Jiang, H., Li, S., & Ma, Q. (2023). Reliability Analysis of High-Voltage Drive Motor Systems in Terms of the Polymorphic Bayesian Network. Mathematics, 11(10), 2378"}
{"Name":"poultry","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Genetics","Nodes":47,"Arcs":41,"Parameters":100,"Avg. Parents":0.87,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.17,"Year":2022,"Journal":"BMC Bioinformatics","Reference":"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"}
{"Name":"project","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Environmental Science","Nodes":21,"Arcs":21,"Parameters":52,"Avg. Parents":1,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.76,"Year":2023,"Journal":"Environment Development and Sustainability","Reference":"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"}
{"Name":"projectmanagement","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Business","Nodes":26,"Arcs":35,"Parameters":100,"Avg. Parents":1.35,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4.62,"Year":2016,"Journal":"International Journal of Project Management","Reference":"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"}
{"Name":"propellant","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Engineering","Nodes":49,"Arcs":48,"Parameters":8345,"Avg. Parents":0.98,"Max Parents":13,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":6.57,"Year":2023,"Journal":"Journal of Loss Prevention in the Process Industries","Reference":"Bi, Y., Wang, S., Zhang, C., Cong, H., Gao, W., Qu, B., & Li, J. (2023). A bayesian network-based safety assessment method for solid propellant granule-casting molding process. Journal of Loss Prevention in the Process Industries, 83, 105089"}
{"Name":"rainstorm","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Earth Sciences","Nodes":34,"Arcs":33,"Parameters":96,"Avg. Parents":0.97,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.24,"Year":2023,"Journal":"Natural Hazards","Reference":"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."}
{"Name":"rainwater","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"TAN","Area":"Environmental Science","Nodes":6,"Arcs":6,"Parameters":107,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":4.5,"Max Levels":12,"Average Markov Blanket":2,"Year":2019,"Journal":"Resources, Conservation and Recycling","Reference":"Geraldi, M. S., & Ghisi, E. (2019). Short-term instead of long-term rainfall time series in rainwater harvesting simulation in houses: An assessment using Bayesian Network. Resources, Conservation and Recycling, 144, 1-12"}
{"Name":"redmeat","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Psychology","Nodes":11,"Arcs":11,"Parameters":4555,"Avg. Parents":1,"Max Parents":6,"Avg. Levels":3.27,"Max Levels":5,"Average Markov Blanket":6.36,"Year":2022,"Journal":"Frontiers in Psychology","Reference":"Catellani, P., Carfora, V., & Piastra, M. (2022). Framing and tailoring prefactual messages to reduce red meat consumption: Predicting effects through a psychology-based graphical causal model. Frontiers in Psychology, 13, 825602"}
{"Name":"resilience","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Environmental Science","Nodes":36,"Arcs":52,"Parameters":521,"Avg. Parents":1.44,"Max Parents":6,"Avg. Levels":2.14,"Max Levels":3,"Average Markov Blanket":7.17,"Year":2022,"Journal":"Frontiers in Forests and Global Change","Reference":"Franco-Gaviria, F., Amador-Jimenez, M., Millner, N., Durden, C., & Urrego, D. H. (2022). Quantifying resilience of socio-ecological systems through dynamic Bayesian networks. Frontiers in Forests and Global Change, 5, 889274"}
{"Name":"ricci","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Social Sciences","Nodes":6,"Arcs":6,"Parameters":13,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2,"Year":2022,"Journal":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery","Reference":"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"}
{"Name":"rockburst","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"TAN","Area":"Engineering","Nodes":6,"Arcs":9,"Parameters":53,"Avg. Parents":1.5,"Max Parents":2,"Avg. Levels":2.83,"Max Levels":3,"Average Markov Blanket":3,"Year":2021,"Journal":"Engineering Science and Technology","Reference":"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"}
{"Name":"rockquality","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Reverse Tree","Area":"Earth Sciences","Nodes":9,"Arcs":8,"Parameters":537,"Avg. Parents":0.89,"Max Parents":3,"Avg. Levels":4.67,"Max Levels":5,"Average Markov Blanket":2.89,"Year":2022,"Journal":"Scientific Reports","Reference":"Ma, J., Li, T., Li, X., Zhou, S., Ma, C., Wei, D., & Dai, K. (2022). A probability prediction method for the classification of surrounding rock quality of tunnels with incomplete data using Bayesian networks. Scientific Reports, 12(1), 19846"}
{"Name":"ropesegment","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Reverse Tree","Area":"Engineering","Nodes":11,"Arcs":10,"Parameters":31,"Avg. Parents":0.91,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.27,"Year":2024,"Journal":"Journal of The Institution of Engineers","Reference":"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"}
{"Name":"safespeeds","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"TAN","Area":"Social Sciences","Nodes":9,"Arcs":15,"Parameters":1207,"Avg. Parents":1.67,"Max Parents":2,"Avg. Levels":4.78,"Max Levels":11,"Average Markov Blanket":3.33,"Year":2024,"Journal":"Transportation Research Interdisciplinary Perspectives","Reference":"Vos, J., Farah, H., & Hagenzieker, M. (2024). Modelling driver expectations for safe speeds on freeway curves using Bayesian belief networks. Transportation Research Interdisciplinary Perspectives, 27, 101178"}
{"Name":"sallyclark","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Tree","Area":"Law","Nodes":6,"Arcs":5,"Parameters":11,"Avg. Parents":0.83,"Max Parents":1,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":1.67,"Year":2023,"Journal":"Artificial Intelligence and Law","Reference":"Kowalewska, A., & Urbaniak, R. (2023). Measuring coherence with Bayesian networks. Artificial Intelligence and Law, 31(2), 369-395"}
{"Name":"samonella1","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Veterinary","Nodes":7,"Arcs":5,"Parameters":13,"Avg. Parents":0.71,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":1.43,"Year":2022,"Journal":"BMC Veterinary Research","Reference":"Teng, K. T. Y., Aerts, M., Jaspers, S., Ugarte-Ruiz, M., Moreno, M. A., Saez, J. L., ... & Alvarez, J. (2022). Patterns of antimicrobial resistance in Salmonella isolates from fattening pigs in Spain. BMC Veterinary Research, 18(1), 333"}
{"Name":"samonella2","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Veterinary","Nodes":10,"Arcs":10,"Parameters":21,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2,"Year":2022,"Journal":"BMC Veterinary Research","Reference":"Teng, K. T. Y., Aerts, M., Jaspers, S., Ugarte-Ruiz, M., Moreno, M. A., Saez, J. L., ... & Alvarez, J. (2022). Patterns of antimicrobial resistance in Salmonella isolates from fattening pigs in Spain. BMC Veterinary Research, 18(1), 333"}
{"Name":"seismic","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Engineering","Nodes":10,"Arcs":12,"Parameters":164,"Avg. Parents":1.2,"Max Parents":3,"Avg. Levels":3,"Max Levels":3,"Average Markov Blanket":3.4,"Year":2024,"Journal":"Asian Journal of Civil Engineering","Reference":"Roy, G., Sen, M. K., Singh, A., Dutta, S., & Choudhury, S. (2024). Probabilistic seismic risk assessment of a reinforced concrete building considering hazard level and the resulting vulnerability using Bayesian Belief Network. Asian Journal of Civil Engineering, 25(3), 2993-3009"}
{"Name":"shipping","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Reverse Tree","Area":"Engineering","Nodes":36,"Arcs":35,"Parameters":114,"Avg. Parents":0.97,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.61,"Year":2023,"Journal":"Marine Science and Engineering","Reference":"Chang, Z., He, X., Fan, H., Guan, W., & He, L. (2023). Leverage Bayesian network and fault tree method on risk assessment of LNG maritime transport shipping routes: Application to the China-Australia route. Journal of Marine Science and Engineering, 11(9), 1722"}
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{"Name":"softwarelogs1","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Computer Science","Nodes":5,"Arcs":5,"Parameters":831,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":14,"Max Levels":42,"Average Markov Blanket":2,"Year":2022,"Journal":"IET Software","Reference":"del Rey, S., Martinez-Fernandez, S., & Salmeron, A. (2023). Bayesian Network analysis of software logs for data-driven software maintenance. IET Software, 17(3), 268-286"}
{"Name":"softwarelogs2","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Computer Science","Nodes":40,"Arcs":67,"Parameters":32119,"Avg. Parents":1.68,"Max Parents":2,"Avg. Levels":14,"Max Levels":44,"Average Markov Blanket":4.75,"Year":2022,"Journal":"IET Software","Reference":"del Rey, S., Martinez-Fernandez, S., & Salmeron, A. (2023). Bayesian Network analysis of software logs for data-driven software maintenance. IET Software, 17(3), 268-286"}
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{"Name":"soil","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Earth Sciences","Nodes":6,"Arcs":6,"Parameters":13,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":2.33,"Year":2020,"Journal":"Arabian Journal of Geosciences","Reference":"Ben Amor, H., Elaoud, A., Ben Hassen, H., Ben Salah, N., Masmoudi, A., & Elmoueddeb, K. (2020). Characteristic study of some parameters of soil irrigated by magnetized waters. Arabian Journal of Geosciences, 13, 1-11"}
{"Name":"soillead","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Environmental Science","Nodes":9,"Arcs":22,"Parameters":353,"Avg. Parents":2.44,"Max Parents":4,"Avg. Levels":2.89,"Max Levels":3,"Average Markov Blanket":5.56,"Year":2021,"Journal":"Environmental Science & Technology","Reference":"Obeng-Gyasi, E., Roostaei, J., & Gibson, J. M. (2021). Lead distribution in urban soil in a medium-sized city: household-scale analysis. Environmental Science & Technology, 55(6), 3696-3705"}
{"Name":"soilliquefaction1","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Reverse Naive Bayes","Area":"Engineering","Nodes":7,"Arcs":6,"Parameters":4114,"Avg. Parents":0.86,"Max Parents":6,"Avg. Levels":3.71,"Max Levels":4,"Average Markov Blanket":6,"Year":2021,"Journal":"Frontiers of Structural and Civil Engineering","Reference":"Ahmad, M., Tang, X. W., Qiu, J. N., Ahmad, F., & Gu, W. J. (2021). Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential. Frontiers of Structural and Civil Engineering, 15, 490-505"}
{"Name":"soilliquefaction2","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Reverse Tree","Area":"Engineering","Nodes":7,"Arcs":6,"Parameters":1051,"Avg. Parents":0.86,"Max Parents":5,"Avg. Levels":3.71,"Max Levels":4,"Average Markov Blanket":4.57,"Year":2021,"Journal":"Frontiers of Structural and Civil Engineering","Reference":"Ahmad, M., Tang, X. W., Qiu, J. N., Ahmad, F., & Gu, W. J. (2021). Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential. Frontiers of Structural and Civil Engineering, 15, 490-505"}
{"Name":"soilliquefaction3","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Engineering","Nodes":7,"Arcs":12,"Parameters":4222,"Avg. Parents":1.71,"Max Parents":6,"Avg. Levels":3.71,"Max Levels":4,"Average Markov Blanket":6,"Year":2021,"Journal":"Frontiers of Structural and Civil Engineering","Reference":"Ahmad, M., Tang, X. W., Qiu, J. N., Ahmad, F., & Gu, W. J. (2021). Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential. Frontiers of Structural and Civil Engineering, 15, 490-505"}
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{"Name":"stocks","Type":"Gaussian","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Economics","Nodes":13,"Arcs":23,"Parameters":49,"Avg. Parents":1.77,"Max Parents":5,"Average Markov Blanket":5.38,"Year":2024,"Journal":"Heliyon","Reference":"Sener, E., & Demir, I. (2024). Gaussian Bayesian network model of healthcare, food and energy sectors in the pandemic: Turkiye case. Heliyon, 10(1)"}
{"Name":"student1","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Social Sciences","Nodes":26,"Arcs":27,"Parameters":192,"Avg. Parents":1.04,"Max Parents":2,"Avg. Levels":3.08,"Max Levels":8,"Average Markov Blanket":2.31,"Year":2022,"Journal":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery","Reference":"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"}
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{"Name":"suffocation","Type":"Gaussian","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Reverse Tree","Area":"Engineering","Nodes":35,"Arcs":34,"Parameters":104,"Avg. Parents":0.97,"Max Parents":12,"Average Markov Blanket":9.37,"Year":2022,"Journal":"Applied Sciences","Reference":"Qiao, W., Guo, H., Huang, E., Deng, W., Lian, C., & Chen, H. (2022). Human-Related Hazardous Events Assessment for Suffocation on Ships by Integrating Bayesian Network and Complex Network. Applied Sciences, 12(14), 6905"}
{"Name":"tastingtea","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Social Sciences","Nodes":17,"Arcs":72,"Parameters":1787,"Avg. Parents":4.24,"Max Parents":9,"Avg. Levels":2.06,"Max Levels":3,"Average Markov Blanket":12.71,"Year":2024,"Journal":"PloS One","Reference":"Xie, G. (2024). A Bayesian network for modelling the Lady tasting tea experiment. PloS one, 19(7), e0307866."}
{"Name":"tbm","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Generic","Area":"Earth Sciences","Nodes":10,"Arcs":15,"Parameters":4250,"Avg. Parents":1.5,"Max Parents":6,"Avg. Levels":3.2,"Max Levels":4,"Average Markov Blanket":6.6,"Year":2022,"Journal":"Bulletin of Engineering Geology and the Environment","Reference":"Lin, P., Xiong, Y., Xu, Z., Wang, W., & Shao, R. (2022). Risk assessment of TBM jamming based on Bayesian networks. Bulletin of Engineering Geology and the Environment, 81, 1-15"}
{"Name":"theft1","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Naive Bayes","Area":"Law","Nodes":4,"Arcs":3,"Parameters":7,"Avg. Parents":0.75,"Max Parents":1,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":1.5,"Year":2023,"Journal":"Legal Knowledge and Information Systems","Reference":"van Leeuwen, L., Verheij, B., Verbrugge, R., & Renooij, S. (2023). Evaluating Methods for Setting a Prior Probability of Guilt. In Legal Knowledge and Information Systems (pp. 63-72). IOS Press"}
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{"Name":"titanic","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Social Sciences","Nodes":4,"Arcs":5,"Parameters":23,"Avg. Parents":1.25,"Max Parents":2,"Avg. Levels":2.5,"Max Levels":4,"Average Markov Blanket":2.5,"Year":2022,"Journal":"Journal of Statistical Software","Reference":"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"}
{"Name":"trajectories","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Medicine","Nodes":5,"Arcs":9,"Parameters":70,"Avg. Parents":1.8,"Max Parents":4,"Avg. Levels":2.4,"Max Levels":3,"Average Markov Blanket":4,"Year":2022,"Journal":"International Conference in Artificial Intelligence and Statistics","Reference":"Leonelli, M., & Varando, G. (2023, April). Context-specific causal discovery for categorical data using staged trees. In International Conference on Artificial Intelligence and Statistics (pp. 8871-8888). PMLR"}
{"Name":"transport","Type":"Discrete","Structure":"Synthetic","Probabilities":"Synthetic","Graph":"Generic","Area":"Social Sciences","Nodes":6,"Arcs":6,"Parameters":21,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2.33,"Max Levels":3,"Average Markov Blanket":2.67,"Year":2014,"Journal":"Book","Reference":"Scutari, M., & Denis, J. B. (2014). Bayesian networks: with examples in R. Chapman and Hall/CRC"}
{"Name":"tubercolosis","Type":"Discrete","Structure":"Knowledge","Probabilities":"Data","Graph":"Reverse Tree","Area":"Medicine","Nodes":5,"Arcs":4,"Parameters":25,"Avg. Parents":0.8,"Max Parents":3,"Avg. Levels":2.4,"Max Levels":3,"Average Markov Blanket":2.8,"Year":2021,"Journal":"Computational Science and Its Applications","Reference":"Razwiedani, M., & Kogeda, O. P. (2021). A Decision Support System for Tuberculosis Prevalence in South Africa. In Computational Science and Its Applications. Springer International Publishing"}
{"Name":"turbine1","Type":"Gaussian","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Engineering","Nodes":16,"Arcs":20,"Parameters":52,"Avg. Parents":1.25,"Max Parents":3,"Average Markov Blanket":3.38,"Year":2022,"Journal":"Ocean Engineering","Reference":"Rostam-Alilou, A. A., Zhang, C., Salboukh, F., & Gunes, O. (2022). Potential use of Bayesian Networks for estimating relationship among rotational dynamics of floating offshore wind turbine tower in extreme environmental conditions. Ocean Engineering, 244, 110230"}
{"Name":"turbine2","Type":"Gaussian","Structure":"Data","Probabilities":"Data","Graph":"Generic","Area":"Engineering","Nodes":16,"Arcs":20,"Parameters":52,"Avg. Parents":1.25,"Max Parents":3,"Average Markov Blanket":3.38,"Year":2022,"Journal":"Ocean Engineering","Reference":"Rostam-Alilou, A. A., Zhang, C., Salboukh, F., & Gunes, O. (2022). Potential use of Bayesian Networks for estimating relationship among rotational dynamics of floating offshore wind turbine tower in extreme environmental conditions. Ocean Engineering, 244, 110230"}
{"Name":"twinframework","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Engineering","Nodes":7,"Arcs":7,"Parameters":19,"Avg. Parents":1,"Max Parents":3,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.14,"Year":2023,"Journal":"Sensors","Reference":"Jiao, Z., Du, X., Liu, Z., Liu, L., Sun, Z., & Shi, G. (2023). Sustainable Operation and Maintenance Modeling and Application of Building Infrastructures Combined with Digital Twin Framework. Sensors, 23(9), 4182"}
{"Name":"urinary","Type":"Discrete","Structure":"Expert","Probabilities":"Expert","Graph":"Generic","Area":"Medicine","Nodes":36,"Arcs":109,"Parameters":2771,"Avg. Parents":3.03,"Max Parents":6,"Avg. Levels":2.58,"Max Levels":4,"Average Markov Blanket":7.22,"Year":2023,"Journal":"BMC Medical Research Methodology","Reference":"Ramsay, J. A., Mascaro, S., Campbell, A. J., Foley, D. A., Mace, A. O., Ingram, P., ... & Wu, Y. (2022). Urinary tract infections in children: building a causal model-based decision support tool for diagnosis with domain knowledge and prospective data. BMC Medical Research Methodology, 22(1), 218"}
{"Name":"vaccine","Type":"Discrete","Structure":"Synthetic","Probabilities":"Synthetic","Graph":"Generic","Area":"Medicine","Nodes":3,"Arcs":3,"Parameters":11,"Avg. Parents":1,"Max Parents":2,"Avg. Levels":2.33,"Max Levels":3,"Average Markov Blanket":2,"Year":2017,"Journal":"Information Sciences","Reference":"Leonelli, M., Gorgen, C., & Smith, J. Q. (2017). Sensitivity analysis in multilinear probabilistic models. Information Sciences, 411, 84-97"}
{"Name":"vessel1","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Engineering","Nodes":16,"Arcs":17,"Parameters":53,"Avg. Parents":1.06,"Max Parents":4,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":3.75,"Year":2020,"Journal":"Ocean Engineering","Reference":"Ugurlu, F., Yildiz, S., Boran, M., Ugurlu, O., & Wang, J. (2020). Analysis of fishing vessel accidents with Bayesian network and Chi-square methods. Ocean Engineering, 198, 106956"}
{"Name":"vessel2","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Generic","Area":"Engineering","Nodes":13,"Arcs":18,"Parameters":63,"Avg. Parents":1.38,"Max Parents":4,"Avg. Levels":2.08,"Max Levels":3,"Average Markov Blanket":4.62,"Year":2020,"Journal":"Ocean Engineering","Reference":"Ugurlu, F., Yildiz, S., Boran, M., Ugurlu, O., & Wang, J. (2020). Analysis of fishing vessel accidents with Bayesian network and Chi-square methods. Ocean Engineering, 198, 106956"}
{"Name":"waterlead","Type":"Discrete","Structure":"Data","Probabilities":"Data","Graph":"TAN","Area":"Environmental Science","Nodes":17,"Arcs":31,"Parameters":145,"Avg. Parents":1.82,"Max Parents":2,"Avg. Levels":2.65,"Max Levels":4,"Average Markov Blanket":3.65,"Year":2023,"Journal":"Environmental Science & Technology","Reference":"Mulhern, R. E., Kondash, A. J., Norman, E., Johnson, J., Levine, K., McWilliams, A., ... & Hoponick Redmon, J. (2023). Improved decision making for water lead testing in US child care facilities using machine-learned Bayesian networks. Environmental Science & Technology, 57(46), 17959-17970"}
{"Name":"wheat","Type":"Discrete","Structure":"Fixed","Probabilities":"Expert","Graph":"Reverse Naive Bayes","Area":"Agricultural Sciences","Nodes":5,"Arcs":4,"Parameters":393,"Avg. Parents":0.8,"Max Parents":4,"Avg. Levels":3.6,"Max Levels":5,"Average Markov Blanket":4,"Year":2023,"Journal":"Smart Agricultural Technology","Reference":"Mahmood, S. A., Karampoiki, M., Hammond, J. P., Paraforos, D. S., Murdoch, A. J., & Todman, L. (2023). Embedding expert opinion in a Bayesian network model to predict wheat yield from spring-summer weather. Smart Agricultural Technology, 4, 100224"}
{"Name":"windturbine","Type":"Discrete","Structure":"Knowledge","Probabilities":"Mixed","Graph":"Generic","Area":"Engineering","Nodes":122,"Arcs":123,"Parameters":520,"Avg. Parents":1.01,"Max Parents":6,"Avg. Levels":2,"Max Levels":2,"Average Markov Blanket":4.33,"Year":2020,"Journal":"Ocean Engineering","Reference":"Li, H., Soares, C. G., & Huang, H. Z. (2020). Reliability analysis of a floating offshore wind turbine using Bayesian Networks. Ocean Engineering, 217, 107827"}
{"Name":"witness","Type":"Discrete","Structure":"Knowledge","Probabilities":"Knowledge","Graph":"Naive Bayes","Area":"Law","Nodes":6,"Arcs":5,"Parameters":35,"Avg. Parents":0.83,"Max Parents":1,"Avg. Levels":2.67,"Max Levels":6,"Average Markov Blanket":1.67,"Year":2023,"Journal":"Artificial Intelligence and Law","Reference":"Kowalewska, A., & Urbaniak, R. (2023). Measuring coherence with Bayesian networks. Artificial Intelligence and Law, 31(2), 369-395"}
{"Name":"yangtze","Type":"Discrete","Structure":"Knowledge","Probabilities":"Expert","Graph":"Generic","Area":"Engineering","Nodes":31,"Arcs":54,"Parameters":397,"Avg. Parents":1.74,"Max Parents":5,"Avg. Levels":2.19,"Max Levels":3,"Average Markov Blanket":7.1,"Year":2023,"Journal":"Ocean Engineering","Reference":"Fu, S., Gu, S., Zhang, Y., Zhang, M., & Weng, J. (2023). Towards system-theoretic risk management for maritime transportation systems: A case study of the yangtze river estuary. Ocean Engineering, 286, 115637"}