Dynamic Bayesian networks are Bayesian networks which explicitly incorporating the dimension of time. They are distinguished into repetitive and non-repetitive networks. Repetitive networks have the same set of random (statistical) variables and independence relations at each time step, whereas in non-repetitive networks the set of random variables and the independence relations between these r...

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In this paper we study a probabilistic approach to characterize Interpersonal Behaviours (IBs) in a social concept by exploring the existent interrelation between body motion features. Human activities were explored in different level of complexities, such as social-based human activity. To bridge the existent big gap between human body motions and the IBs analysis, a set of proper dependencies...

the planning of water resources is based on the volume of water extracted from the aquifer and accurate estimate of this volume considerably helps to development. in this study, the bayesian networks model using continues and clustering structures was used to simulate the groundwater level of birjand aquifer. bayesian networks was calibrated with five input variables of aquifer recharge, water ...

This paper investigates the issue of degeneracy in student modeling with Dynamic Bayesian Network in Prime Climb, an intelligent educational game for practicing number factorization. We discuss that maximizing the common measure of predictive accuracy (i.e. end accuracy) of the student model may not necessarily ensure trusted assessment of learning in the student and that, it could result in im...