What question did this study set out to answer?

The aim is to examine how Bayesian network structures are constructed and learned in applied research, focusing on the role of expert knowledge and data sharing.

July 26, 2026MathematicsOpen Access

Applied Bayesian Networks Rely on Expert Knowledge and Scarce Data Sharing

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Authors

LCLiam CoorssenBrock University HKHamid KalantariUniversity of Alberta PAParham AfsharniaInstitute for Advanced Studies in Basic Sciences

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Descriptive scoping review assesses Bayesian network structures using expert knowledge and data sharing.

Key Points

The aim is to examine how Bayesian network structures are constructed and learned in applied research, focusing on the role of expert knowledge and data sharing.
Reviewed 5993 papers from 2020–2025 mentioning 'Bayesian (belief) network', narrowing down to 3661 relevant ones.
Categorized usage of expert knowledge and algorithms in constructing Bayesian networks.
Curated a benchmark index from 129 studies that provided functional dataset links.
56.2% of relevant papers (2059) utilized expert knowledge, with 48.8% relying solely on it.
Only 3.5% of studies shared functional datasets, highlighting a scarcity in data sharing.
Among papers using algorithms without expert knowledge, score-based methods were predominant, comprising 72.1% of those methods.

Cite This Study

Coorssen et al. (2026) studied this question.

synapsesocial.com/papers/6a65a551d3aea3239cd776e6 https://doi.org/10.3390/math14152669

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Applied Bayesian Networks Rely on Expert Knowledge and Scarce Data Sharing

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Member takes

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Key Points

Cite This Study

Applied Bayesian Networks Rely on Expert Knowledge and Scarce Data Sharing

Discussion

Member takes

Implication

Key Points

Cite This Study

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