This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent real world processes that include several distinct phases. In essence a GBN is a model that combines several Bayesian networks (BN) in such a manner that they may be active or inactive during queries to the model. We use objects called gates to combine BNs, and to activate and deactivate them when predefined logical statements are satisfied. These statements are based on combinations of posterior probabilities of the variables in the BNs. Although GBN is a new formalism there are features of GBNs that are similar to other formalisms and research, including influence diagrams, context-specific independence and structural adaptation.