Temporal Bayesian Networks
Department of Computational Science, University of Saskatchewan Saskatoon, Saskatchewan, Canada S7N 0W0
Temporal formalisms are useful in several applications such as planning, scheduling and diagnosis. Probabilistic temporal reasoning emerged to deal with the uncertainties usually encountered in such applications. Bayesian networks provide a simple compact graphical representation of a probability distribution by exploiting conditional independencies. This paper presents a simple technique for representing time in Bayesian networks by expressing probabilities as fun
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