Fro.GBN

<code>Fro.GBN</code> returns the Frobenius norm between between an object of class <code>GBN</code> and its update after a standard 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

Fro.GBN function

<dl><dt>x</dt>
<dd>object of class <code>GBN</code>.</dd>
<dt>entry</dt>
<dd>a vector of length 2 indicating the entry of the covariance matrix to vary.</dd>
<dt>delta</dt>
<dd>numeric vector, including the variation parameters that act additively.</dd>
<dt>log</dt>
<dd>boolean value. If <code>TRUE</code>, the logarithm of the Frobenius norm is returned. Set by default to <code>TRUE</code>.</dd>
<dt>...</dt>
<dd>additional arguments for compatibility.</dd></dl>

Arguments

Frobenius norm for GBN — Fro.GBN

<dl>

<dt>x</dt>
<dd>object of class <code>GBN</code>.</dd>


<dt>entry</dt>
<dd>a vector of length 2 indicating the entry of the covariance matrix to vary.</dd>


<dt>delta</dt>
<dd>numeric vector, including the variation parameters that act additively.</dd>


<dt>log</dt>
<dd>boolean value. If <code>TRUE</code>, the logarithm of the Frobenius norm is returned. Set by default to <code>TRUE</code>.</dd>


<dt>...</dt>
<dd>additional arguments for compatibility.</dd>

</dl>

Fro.GBN: Frobenius norm for `GBN`

Description

Usage

Value

Arguments

Details

See Also

Examples