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    Author(s): Bruce G. Marcot; Trent D. Penman
    Date: 2019
    Source: Environmental Modelling & Software. 111: 386-393.
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Northwest Research Station
    PDF: Download Publication  (746.0 KB)

    Description

    Bayesian network (BN) modeling is a rapidly advancing field. Here we explore new methods by which BN model development and application are being joined with other tools and model frameworks. Advances include improving areas of Bayesian classifiers and machine-learning algorithms for model structuring and parameterization, and development of time-dynamic models. Increasingly, BN models are being integrated with: management decision networks; structural equation modeling of causal networks; Bayesian neural networks; combined discrete and continuous variables; object-oriented and agent-based models; state-and-transition models; geographic information systems; quantum probability; and other fields. Integrated BNs (IBNs) are becoming useful tools in risk analysis, risk management, and decision science for resource planning and environmental management. In the near future, IBNs may become self-structuring, self-learning systems fed by real-time monitoring data. Such advances may make model validation difficult, and may question model credibility, particularly if based on uncertain sources of knowledge systems and big data.

    Publication Notes

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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.

    Citation

    Marcot, Bruce G.; Penman, Trent D. 2019. Advances in Bayesian network modelling: Integration of modelling technologies. Environmental Modelling & Software. 111: 386-393. https://doi.org/10.1016/j.envsoft.2018.09.016.

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    Keywords

    Bayesian networks, decision models, model integration, machine learning, model validation.

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