Characterization of Dynamic Bayesian Network
In this paper, the authors will be interested at Dynamic Bayesian Network (DBNs) as a model that tries to incorporate temporal dimension with uncertainty. They start with basics of DBN where they especially focus in Inference and Learning concepts and algorithms. Then, they will present different levels and methods of creating DBNs as well as approaches of incorporating temporal dimension in static Bayesian network. The majority of events encountered in everyday life are not well described based on their occurrence at a particular point in time but rather they are described by a set of observations that can produce a comprehensive final event. Thus, time is an important dimension to take into account in reasoning and in the field of artificial intelligence in general.