fire_alarm

<code>fire_alarm</code> is a <code>bn.fit</code> object including a Bayesian network for a fire alarm system.

datasets

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

fire_alarm function

The Bayesian network <code>fire_alarm</code> includes the following nodes:<ul>
<li>Fire: two-level factor with levels <code>TRUE</code> and <code>FALSE</code>. It indicates presence or absence of a fire.</li>
<li>Smoke: two level-factor with levels <code>TRUE</code> and <code>FALSE</code>. It indicates presence or absence of smoke.</li>
<li>Alarm: three level-factor with levels <code>TRUE</code>, <code>MALFUNCTION</code> and <code>FALSE</code>. It indicates if the alarm is ringing, malfunctioning or not ringing.</li>
<li>Tampering: two level-factor with levels <code>TRUE</code> and <code>FALSE</code>. It indicates if the alarm system has been tampered or not.</li>
<li>Leaving: two level-factor with levels <code>TRUE</code> and <code>FALSE</code>. It indicates if the building is being evacuated or not.</li>
<li>Report: two level-factor with levels <code>TRUE</code> and <code>FALSE</code>. It indicates if the incident has been reported or not.</li>
</ul>

fire_alarm: Bayesian network on fire alarm system

Description

Usage

Arguments

Format