Corpus ID: 236428846

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

@article{Leonelli2021SensitivityAR,
  title={Sensitivity and robustness analysis in Bayesian networks with the bnmonitor R package},
  author={Manuele Leonelli and Ramsiya Ramanathan and Rachel Lynne Wilkerson},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.11785}
}
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… Expand

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References

SHOWING 1-10 OF 41 REFERENCES
Good practice in Bayesian network modelling
TLDR
This paper provides guidelines to developing and evaluating Bayesian network models of environmental systems, and presents a case study habitat suitability model for juvenile Astacopsis gouldi, the giant freshwater crayfish of Tasmania. Expand
Modeling Operational Risk with Bayesian Networks
TLDR
This model shows how established Bayesian network methodology can be applied to form posterior marginal distributions of variables based on evidence, simulate scenarios, update the parameters of the model using data, and quantify in real-time how well the model predictions compare to actual data. Expand
Application of Bayesian Networks in Reliability Evaluation
TLDR
A bibliographic review of BNs that have been proposed for reliability evaluation in the last decades is presented, and a few upcoming research directions that are of interest to reliability researchers are identified. Expand
Learning Bayesian Networks with the bnlearn R Package
TLDR
Bbnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Expand
Comparative analysis of discretization methods in Bayesian networks
TLDR
It is suggested that discretization be used with caution or be avoided, when possible, and the BN models be modified to accommodate continuous variables. Expand
A proposed validation framework for expert elicited Bayesian Networks
TLDR
It is demonstrated that even in cases where no data exist at all there is a broad range of validity tests that can be used to establish confidence in the validity of a Bayesian Belief Network. Expand
Bayesian Networks in Healthcare: Distribution by Medical Condition
TLDR
It is found that almost two-thirds of all healthcare BNs are focused on four conditions: cardiac, cancer, psychological and lung disorders, and there is a lack of understanding regarding how BNs work and what they are capable of. Expand
Sensitivity analysis in multilinear probabilistic models
TLDR
The algebraic approach enables us to prove that for models whose defining polynomial is multilinear both the Chan–Darwiche distance and any divergence in the family of ϕ-divergences are minimized for a certain class of multi-parameter contemporaneous variations when parameters are proportionally covaried. Expand
Bayesian Diagnostics for Chain Event Graphs
Chain event graphs have been established as a practical Bayesian graphical tool. While bespoke diagnostics have been developed for Bayesian Networks, they have not yet been defined for theExpand
BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks
The BayesNetBP package has been developed for probabilistic reasoning and visualization in Bayesian networks with nodes that are purely discrete, continuous or mixed (discrete and continuous).Expand
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