bnmonitor (version 0.2.2)

KL.CI: KL Divergence for CI

Description

KL.CI returns the Kullback-Leibler (KL) divergence between an object of class CI and its update after a model-preserving parameter variation.

Usage

# S3 method for CI
KL(x, type, entry, delta, ...)

Value

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

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 KL 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 KL divergence between a Bayesian network and its updated version after a model-preserving variation.

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

Examples

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