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Bayesian network models for incomplete and dynamic data

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  • Marco Scutari

Abstract

Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper, we will review how Bayesian networks can model dynamic data and data with incomplete observations. Such data are the norm at the forefront of research and in practical applications, and Bayesian networks are uniquely positioned to model them due to their explainability and interpretability.

Suggested Citation

  • Marco Scutari, 2020. "Bayesian network models for incomplete and dynamic data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 397-419, August.
    • Handle: RePEc:bla:stanee:v:74:y:2020:i:3:p:397-419
    DOI: 10.1111/stan.12197
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    References listed on IDEAS

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    2. Roma, Giovanni & Di Maio, Francesco & Zio, Enrico, 2024. "A condition-informed dynamic Bayesian network framework to support severe accident management in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 252(C).

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