Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package
This work addresses the need for validation tools in Bayesian network modeling, particularly for risk assessment in domains like medicine, but it is incremental as it builds on existing software and methods.
The authors tackled the problem of validating Bayesian networks for practical risk assessment by introducing the bnmonitor R package, which is the first comprehensive software for this purpose, as demonstrated through an applied data analysis on a medical dataset.
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.