Brendan Burns, Clayton T. Morrison, and Paul Cohen
A current popular approach to representing time in Bayesian belief networks is through Dynamic Bayesian Networks (DBNs). DBNs connect sequences of entire Bayes networks, each representing a situation at a snapshot in time. We present an alternative method for incorporating time into Bayesian belief networks that utilizes abstractions of temporal representation. This method maintains the principled Bayesian approach to reasoning under uncertainly, providing explicit representation of sequence and potentially complex temporal relationships, while also decreasing overall network complexity compared to DBNs.