A model for reasoning about persistence and causation
Computational Intelligence
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Temporally Invariant Junction Tree for Inference in Dynamic Bayesian Network
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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This work examines the relevance of uncertain temporal information. A key observation that motivates the analysis presented here is that in the presence of uncertainty, relevance of information degenerates as time evolves. This paper presents an empirical quantitative study of the degeneration of relevance in time-sliced Belief Networks that aims at extending known results. A simple technique for estimating an upper bound on the relevance time is presented. To validate the proposed technique, results of experiments using realistic and synthetic time-sliced belief networks are presented. The results show that the proposed upper bound holds in more than 98% of the experiments. These results have been obtained using a modified version of the dynamic belief networks roll-up algorithm.