Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Sensor validation using dynamic belief networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Space-efficient inference in dynamic probabilistic networks
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
A temporal Bayesian network for diagnosis and prediction
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Artificial Intelligence in Medicine
Learning temporal Bayesian networks for power plant diagnosis
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks
Artificial Intelligence in Medicine
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Temporal Nodes Bayesian Networks (TNBNs) and Networks of Probabilistic Events in Discrete Time (NPEDTs) are two different types of Event Bayesian Networks (EBNs). Both are based on the representation of uncertain events, alternatively to Dynamic Bayesian Networks, which deal with real-world dynamic properties. In a previous work, Arroyo-Figueroa and Sucar applied TNBNs to the diagnosis and prediction of the temporal faults that may occur in the steam generator of a fossil power plant. We present an NPEDT for the same domain, along with a comparative evaluation of the two networks. We examine different methods suggested in the literature for the evaluation of Bayesian networks, analyze their limitations when applied to this temporal domain, and suggest a new evaluation method appropriate for EBNs. In general, the results show that, in this domain, NPEDTs perform better than TNBNs, possibly due to finer time granularity used in the NPEDT.