Continuous time Bayesian networks (CTBNs) represent structured stochastic processes that evolve over continuous time. The methodology is based on earlier work on homogenous Markov processes, extended to capture dependencies among variables representing continuous time processes. We have worked to apply CTBNs to the challenge of reasoning about users’ presence and availability over time. As part of this research, we extended the methodology of CTBNs to allow a large class of phase distributions to be used in place of the exponential distribution that had previously been used. We also formulate and examine the use of classes of cost functions for evaluating the performance of CTBN models in the real world. The cost functions represent the loss associated with inaccuracies in the predicted time and states of transitions of processes.