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    Author(s): Bruce G. Marcot
    Date: 2017
    Source: Ecological Modelling. 358: 1-9.
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Northwest Research Station
    PDF: Download Publication  (1.0 MB)


    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.

    Publication Notes

    • Visit PNW's Publication Request Page to request a hard copy of this publication.
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    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Marcot, Bruce G. 2017. Common quandaries and their practical solutions in Bayesian network modeling. Ecological Modelling. 358: 1-9.


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    Bayesian networks, modeling problems, modeling solutions, bias, machine learning, expert knowledge.

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