mlsanityData to 14 Oct 2026

Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package

arXiv 2107.11785 ↗First posted 25 Jul 2021stat.ME cs.AIcited by 8

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Abstract (arXiv)

Bayesian networks are a class of models that are widely used for risk assessment of complex operational systems. There are now multiple approaches, as well as implemented software, that guide their construction via data learning or expert elicitation. However, a constructed Bayesian network needs to be validated before it can be used for practical risk assessment. Here, we illustrate the usage of the bnmonitor R package: the first comprehensive software for the validation of a Bayesian network. An applied data analysis using bnmonitor is carried out over a medical dataset to illustrate the use of its wide array of functions.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 7 of its 62 references that are in the map. First posted on arXiv 25 Jul 2021.

Builds on

7 of 62 references.

References · 7

  1. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 187
  2. 2020A Tutorial on Learning With Bayesian Networkscited by 57
  3. 2020Bayesian Networks in Healthcare: Distribution by Medical Conditioncited by 23
  4. 2020The R Package stagedtrees for Structural Learning of Stratified Staged Treescited by 17
  5. 2018A geometric characterisation of sensitivity analysis in monomial modelscited by 6
  6. 2018Model-Preserving Sensitivity Analysis for Families of Gaussian Distributionscited by 4
  7. 2019Bayesian Diagnostics for Chain Event Graphscited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 3; 2025: 2.

8 papers.

  1. 2024bnRep: A repository of Bayesian networks from the academic literaturecited by 9
  2. 2022You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networkscited by 4
  3. 2023The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networkscited by 2
  4. 2024Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networkscited by 1
  5. 2024The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networkscited by 1
  6. 2021Global sensitivity analysis in probabilistic graphical modelscited by 0
  7. 2025BayesL: a Logical Framework for the Verification of Bayesian Networkscited by 0
  8. 2025Modeling Psychological Profiles in Volleyball via Mixed-Type Bayesian Networkscited by 0