Comparative Evaluation of Temporal Nodes Bayesian Networks and Networks of Probabilistic Events in Discrete Time

Temporal Nodes Bayesian Networks (TNBNs) and Networks of Probabilistic Events in Discrete Time (NPEDTs) are two different types of Bayesian networks (BNs) for temporal reasoning. Arroyo-Figueroa and Sucar applied TNBNs to an industrial domain: the diagnosis and prediction of the temporal faults that may occur in the steam generator of a fossil power plant. We have recently developed an NPEDT for the same domain. In this paper, we present a comparative evaluation of these two systems. The results show that, in this domain, NPEDTs perform better than TNBNs. The ultimate reason for that seems to be the finer time granularity used in the NPEDT with respect to that of the TNBN. Since families of nodes in a TNBN interact through the general model, only a small number of states can be defined for each node; this limitation is overcome in an NPEDT through the use of temporal noisy gates.