R/fire_alarm.R

In bnmonitor: An Implementation of Sensitivity Analysis in Bayesian Networks

#' Bayesian network on fire alarm system
#'
#' \code{fire_alarm} is a \code{bn.fit} object including a Bayesian network for a fire alarm system.
#'
#' @docType data
#'
#'
#' @usage fire_alarm
#'
#' @format The Bayesian network \code{fire_alarm} includes the following nodes:
#' \itemize{
#'   \item \bold{Fire}: two-level factor with levels \code{TRUE} and \code{FALSE}. It indicates presence or absence of a fire.
#'   \item \bold{Smoke}: two level-factor with levels \code{TRUE} and \code{FALSE}. It indicates presence or absence of smoke.
#'   \item \bold{Alarm}: three level-factor with levels \code{TRUE}, \code{MALFUNCTION} and \code{FALSE}. It indicates if the alarm is ringing, malfunctioning or not ringing.
#'   \item \bold{Tampering}: two level-factor with levels \code{TRUE} and \code{FALSE}. It indicates if the alarm system has been tampered or not.
#'   \item \bold{Leaving}: two level-factor with levels \code{TRUE} and \code{FALSE}. It indicates if the building is being evacuated or not.
#'   \item \bold{Report}: two level-factor with levels \code{TRUE} and \code{FALSE}. It indicates if the incident has been reported or not.
#' }
#'
#' @source Hei Chan, Adnan Darwiche (2002). "When do numbers really matter?". Journal of Artificial Intelligence Research 17 (265-287).
"fire_alarm"