global_monitor

Negative marginal log-likelihood of the model

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

global_monitor function

<dl><dt>dag</dt>
<dd>an object of class <code>bn</code> from the <code>bnlearn</code> package</dd>
<dt>df</dt>
<dd>a base R style dataframe</dd>
<dt>alpha</dt>
<dd>single integer. By default, number of max levels in <code>df</code></dd></dl>

Arguments

Global monitor — global_monitor

<dl>

<dt>dag</dt>
<dd>an object of class <code>bn</code> from the <code>bnlearn</code> package</dd>


<dt>df</dt>
<dd>a base R style dataframe</dd>


<dt>alpha</dt>
<dd>single integer. By default, number of max levels in <code>df</code></dd>

</dl>

global_monitor: Global monitor

Description

Usage

Value

Arguments

References

See Also

Examples