KL.GBN {bnmonitor} | R Documentation |
GBN
KL.GBN
returns the Kullback-Leibler (KL) divergence between an object of class GBN
and its update after a standard parameter variation.
## S3 method for class 'GBN' KL(x, where, entry, delta, ...)
x |
object of class |
where |
character string: either |
entry |
if |
delta |
numeric vector, including the variation parameters that act additively. |
... |
additional arguments for compatibility. |
Computation of the KL divergence between a Bayesian network and the additively perturbed Bayesian network, where the perturbation is either to the mean vector or to the covariance matrix.
A dataframe including in the first column the variations performed and in the second column the corresponding KL divergences.
Gómez-Villegas, M. A., Maín, P., & Susi, R. (2007). Sensitivity analysis in Gaussian Bayesian networks using a divergence measure. Communications in Statistics—Theory and Methods, 36(3), 523-539.
Gómez-Villegas, M. A., Main, P., & Susi, R. (2013). The effect of block parameter perturbations in Gaussian Bayesian networks: Sensitivity and robustness. Information Sciences, 222, 439-458.
KL.CI
, Fro.CI
, Fro.GBN
, Jeffreys.GBN
, Jeffreys.CI
KL(synthetic_gbn,"mean",2,seq(-1,1,0.1)) KL(synthetic_gbn,"covariance",c(3,3),seq(-1,1,0.1))