Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty 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
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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Proposed as a subclass of directed evidential network with conditional belief functions (DEVN), dynamic directed evidential network with conditional belief functions (DDEVN) was introduced as a new approach for modeling systems evolving in time. Considered as an alternative to dynamic Bayesian network and dynamic possibilistic network, this framework enables to reason under uncertainty expressed in the belief function formalism. In this paper, we propose a new propagation algorithm in DDEVNs based on a new computational structure, namely the mixed binary join tree, which is appropriate for making the exact inference in these networks.