Bayesian network models for incomplete and dynamic data

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 thi...

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Bibliographic Details
Published in:Statistica Neerlandica 2020-08, Vol.74 (3), p.397-419
Main Author: Scutari, Marco
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian networks
dynamic data
incomplete data
inference
Networks
structure learning
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Summary: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.
ISSN:0039-0402
1467-9574
DOI:10.1111/stan.12197