Jeffreys.CI {bnmonitor}

Jeffreys Divergence for CI

Description

Jeffreys.CI returns the Jeffreys divergence between an object of class CI and its update after a model-preserving parameter variation.

Usage

## S3 method for class 'CI'
Jeffreys(x, type, entry, delta, ...)

Arguments

x

object of class CI.

type

character string. Type of model-preserving co-variation: either "total", "partial", row,column or all. If all the Jeffreys divergence is computed for every type of co-variation matrix.

entry

a vector of length 2 indicating the entry of the covariance matrix to vary.

delta

numeric vector with positive elements, including the variation parameters that act multiplicatively.

...

additional arguments for compatibility.

Details

Computation of the Jeffreys divergence between a Bayesian network and its updated version after a model-preserving variation.

Value

A dataframe including in the first column the variations performed, and in the following columns the corresponding Jeffreys divergences for the chosen model-preserving co-variations.

References

C. Görgen & M. Leonelli (2020), Model-preserving sensitivity analysis for families of Gaussian distributions. Journal of Machine Learning Research, 21: 1-32.

See Also

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

Examples

Jeffreys(synthetic_ci,"total",c(1,1),seq(0.9,1.1,0.01))
Jeffreys(synthetic_ci,"partial",c(1,4),seq(0.9,1.1,0.01))
Jeffreys(synthetic_ci,"column",c(1,2),seq(0.9,1.1,0.01))
Jeffreys(synthetic_ci,"row",c(3,2),seq(0.9,1.1,0.01))
Jeffreys(synthetic_ci,"all",c(3,2),seq(0.9,1.1,0.01))