Jeffreys.GBN: Jeffreys Divergence for 'GBN'

In bnmonitor: An Implementation of Sensitivity Analysis in Bayesian Networks

Jeffreys Divergence for GBN

Description

Jeffreys.GBN returns the Jeffreys divergence between an object of class GBN and its update after a standard parameter variation.

Usage

## S3 method for class 'GBN'
Jeffreys(x, where, entry, delta, ...)

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.

Value

A dataframe including in the first column the variations performed and in the second column the corresponding Jeffreys divergences.

References

Goergen, C., & Leonelli, M. (2018). Model-preserving sensitivity analysis for families of Gaussian distributions. arXiv preprint arXiv:1809.10794.

See Also

KL.GBNKL.CI, Fro.CI, Fro.GBN, Jeffreys.CI

Examples

Jeffreys(synthetic_gbn,"mean",2,seq(-1,1,0.1))
Jeffreys(synthetic_gbn,"covariance",c(3,3),seq(-1,1,0.1))

bnmonitor documentation built on June 7, 2023, 5:19 p.m.