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

arXiv: https://arxiv.org/abs/2107.11785 · HTML: https://mlsanity.com/paper/2107.11785/

## 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 (most cited first, 7 of 7)

- [Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://mlsanity.com/paper/1302.6815/) (2013)
- [A Tutorial on Learning With Bayesian Networks](https://mlsanity.com/paper/2002.00269/) (2020)
- [Bayesian Networks in Healthcare: Distribution by Medical Condition](https://mlsanity.com/paper/2002.00224/) (2020)
- [The R Package stagedtrees for Structural Learning of Stratified Staged Trees](https://mlsanity.com/paper/2004.06459/) (2020)
- [A geometric characterisation of sensitivity analysis in monomial models](https://mlsanity.com/paper/1901.02058/) (2018)
- [Model-Preserving Sensitivity Analysis for Families of Gaussian Distributions](https://mlsanity.com/paper/1809.10794/) (2018)
- [Bayesian Diagnostics for Chain Event Graphs](https://mlsanity.com/paper/1910.04679/) (2019)

## Built on by (most cited first, 8 of 8)

- [bnRep: A repository of Bayesian networks from the academic literature](https://mlsanity.com/paper/2409.19158/) (2024)
- [You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks](https://mlsanity.com/paper/2206.08687/) (2022)
- [The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks](https://mlsanity.com/paper/2302.00364/) (2023)
- [Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks](https://mlsanity.com/paper/2406.05764/) (2024)
- [The diameter of a stochastic matrix: A new measure for sensitivity analysis in Bayesian networks](https://mlsanity.com/paper/2407.04667/) (2024)
- [Global sensitivity analysis in probabilistic graphical models](https://mlsanity.com/paper/2110.03749/) (2021)
- [BayesL: a Logical Framework for the Verification of Bayesian Networks](https://mlsanity.com/paper/2506.23773/) (2025)
- [Modeling Psychological Profiles in Volleyball via Mixed-Type Bayesian Networks](https://mlsanity.com/paper/2509.22111/) (2025)

## 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.