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In this paper, the authors' system is a Markovien system which they can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians Networks to the dynamic processes. Their objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network.
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