An Implementation of Sensitivity Analysis in Bayesian Networks
Amalgamation of levels
Measures of asymmetric independence
Integration with bn.fit objects from bnlearn
bnmonitor: A package for sensitivity analysis and robustness in Bayesi...
Bayesian networks for a cachexia study
CD-distance
Standard variation of the covariance matrix
Co-variation matrices
Co-variation schemes
Diameters in a Bayesian network
Message for the User
Distance-weigthed influence
Strength of edges in a Bayesian network
Edge-weigthed influence
Final node monitors
Frobenius norm for CI
Frobenius norm for GBN
Frobenius norm
Global monitor
Influential observations
Jeffreys Divergence for CI
Jeffreys Divergence for GBN
Jeffreys Divergence
Bounds for the KL-divergence
KL Divergence for bn.fit
KL Divergence for CI
KL Divergence for GBN
KL Divergence
Standard variation of the mean vector
Model-Preserving co-variation
Mutual information
Node monitor
Plotting methods
Printing methods
Check for positive semi-definiteness after a perturbation
Sensitivity function
Sensitivity of probability query
Sequential node monitors
Sequential parent-child node monitors
A synthetic continuous Bayesian network
An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.
Useful links