<code>KL</code> returns the Kullback-Leibler (KL) divergence between a Bayesian network and its update after parameter variation.

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.

Manuele Leonelli

bnmonitor

An Implementation of Sensitivity Analysis in Bayesian Networks

Ramsiya Ramanathan

Rachel Wilkerson

KL function

<dl><dt>x</dt>
<dd>object of class <code>bn.fit</code>, <code>GBN</code> or <code>CI</code>.</dd>
<dt>...</dt>
<dd>parameters specific to the class used.</dd></dl>

Arguments

KL Divergence — KL

<dl>

<dt>x</dt>
<dd>object of class <code>bn.fit</code>, <code>GBN</code> or <code>CI</code>.</dd>


<dt>...</dt>
<dd>parameters specific to the class used.</dd>

</dl>

KL: KL Divergence

Description

Usage

Value

Arguments

Details

See Also