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.

Examples

Run this code

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))

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