Jeffreys.GBN returns the Jeffreys divergence between an object of class GBN and its update after a standard parameter variation.
Usage
# S3 method for GBN
Jeffreys(x, where, entry, delta, ...)
Value
A dataframe including in the first column the variations performed and in the second column the corresponding Jeffreys divergences.
Arguments
x
object of class GBN.
where
character string: either mean or covariance for variations of the mean vector and covariance matrix respectively.
entry
if where == "mean", entry is the index of the entry of the mean vector to vary. If where == "covariance", entry is a vector of length 2 indicating the entry of the covariance matrix to vary.
delta
numeric vector, including the variation parameters that act additively.
...
additional arguments for compatibility.
Details
Computation of the Jeffreys 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.
References
Goergen, C., & Leonelli, M. (2018). Model-preserving sensitivity analysis for families of Gaussian distributions. arXiv preprint arXiv:1809.10794.