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Common quandaries and their practical solutions in Bayesian network modeling

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  • Marcot, Bruce G.

Abstract

Use and popularity of Bayesian network (BN) modeling has greatly expanded in recent years, but many common problems remain. Here, I summarize key problems in BN model construction and interpretation, along with suggested practical solutions. Problems in BN model construction include parameterizing probability values, variable definition, complex network structures, latent and confounding variables, outlier expert judgments, variable correlation, model peer review, tests of calibration and validation, model overfitting, and modeling wicked problems. Problems in BN model interpretation include objective creep, misconstruing variable influence, conflating correlation with causation, conflating proportion and expectation with probability, and using expert opinion. Solutions are offered for each problem and researchers are urged to innovate and share further solutions.

Suggested Citation

  • Marcot, Bruce G., 2017. "Common quandaries and their practical solutions in Bayesian network modeling," Ecological Modelling, Elsevier, vol. 358(C), pages 1-9.
    • Handle: RePEc:eee:ecomod:v:358:y:2017:i:c:p:1-9
    DOI: 10.1016/j.ecolmodel.2017.05.011
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    References listed on IDEAS

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    1. Marcot, Bruce G., 2012. "Metrics for evaluating performance and uncertainty of Bayesian network models," Ecological Modelling, Elsevier, vol. 230(C), pages 50-62.
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    3. Forio, Marie Anne Eurie & Landuyt, Dries & Bennetsen, Elina & Lock, Koen & Nguyen, Thi Hanh Tien & Ambarita, Minar Naomi Damanik & Musonge, Peace Liz Sasha & Boets, Pieter & Everaert, Gert & Dominguez, 2015. "Bayesian belief network models to analyse and predict ecological water quality in rivers," Ecological Modelling, Elsevier, vol. 312(C), pages 222-238.
    4. repec:cup:judgdm:v:10:y:2015:i:2:p:130-143 is not listed on IDEAS
    5. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    6. Moe, S. Jannicke & Haande, Sigrid & Couture, Raoul-Marie, 2016. "Climate change, cyanobacteria blooms and ecological status of lakes: A Bayesian network approach," Ecological Modelling, Elsevier, vol. 337(C), pages 330-347.
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    1. Helen J Mayfield & Carl S Smith & John H Lowry & Conall H Watson & Michael G Baker & Mike Kama & Eric J Nilles & Colleen L Lau, 2018. "Predictive risk mapping of an environmentally-driven infectious disease using spatial Bayesian networks: A case study of leptospirosis in Fiji," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 12(10), pages 1-16, October.
    2. Mayfield, Helen J. & Bertone, Edoardo & Smith, Carl & Sahin, Oz, 2020. "Use of a structure aware discretisation algorithm for Bayesian networks applied to water quality predictions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 175(C), pages 192-201.
    3. Rui Han & Shiqi Yang, 2023. "A Study on Industrial Heritage Renewal Strategy Based on Hybrid Bayesian Network," Sustainability, MDPI, vol. 15(13), pages 1-32, July.
    4. Solveig Höfer & Alex Ziemba & Ghada El Serafy, 2020. "A Bayesian approach to ecosystem service trade-off analysis utilizing expert knowledge," Environment Systems and Decisions, Springer, vol. 40(1), pages 67-83, March.

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