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
Planning and control
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
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Optimal decomposition of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
The BATmobile: towards a Bayesian automated taxi
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
On triangulating dynamic graphical models
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Dynamic Bayesian networks (DBNs) extend Bayesian networks from static domains to dynamic domains. The only known generic method for exact inference in DBNs is based on dynamic expansion and reduction of active slices. It is effective when the domain evolves relatively slowly, but is reported to be "too expensive" for fast evolving domain where inference is under time pressure. This study explores the stationary feature of problem domains to improve the efficiency of exact inference in DBNs. We propose the construction of a temporally invariant template of a DBN directly supporting exact inference and discuss issues in the construction. This method eliminates the need for the computation associated with dynamic expansion and reduction of the existing method. The method is demonstrated by experimental result.