Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Networks for Reliability Analysis of Complex Systems
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Inference in directed evidential networks based on the transferable belief model
International Journal of Approximate Reasoning
Comparing evidential graphical models for imprecise reliability
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Evidential reasoning with conditional belief functions
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
New propagation algorithm in dynamic directed evidential networks with conditional belief functions
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
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Directed evidential graphical models are important tools for handling uncertain information in the framework of evidence theory. They obtain their efficiency by compactly representing (in)dependencies between variables in the network and efficiently reasoning under uncertainty. This paper presents a new dynamic evidential network for representing uncertainty and managing temporal changes in data. This proposed model offers an alternative framework for dynamic probabilistic and dynamic possibilistic networks. A complexity study of representation and reasoning in the proposed model is also presented in this paper.