KL.CI {bnmonitor} | R Documentation |
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 class 'CI'
KL(x, type, entry, delta, ...)
Arguments
x |
object of class |
type |
character string. Type of model-preserving co-variation: either |
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.
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.
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))