Expectation Propagation for Continuous Time Bayesian Networks (2005)

by Uri Nodelman, Daphne Koller, and Christian R. Shelton

Abstract: Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. As shown previously, exact inference in CTBNs is intractable. We address the problem of approximate inference, allowing for general queries conditioned on evidence over continuous time intervals and at discrete time points. We show how CTBNs can be parameterized within the exponential family, and use that insight to develop a message passing scheme in cluster graphs and allows us to apply expectation propagation to CTBNs. The clusters in our cluster graph representation do not contain distributions over the cluster variables at individual time points, but distributions over trajectories of the variables throughout a duration. Thus, unlike discrete time temporal models such as dynamic Bayesian networks, we can adapt the time granularity at which we reason for different variables in different conditions.

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Uri Nodelman, Daphne Koller, and Christian R. Shelton (2005). "Expectation Propagation for Continuous Time Bayesian Networks." Proceedings of the Twenty-First International Conference on Uncertainty in Artificial Intelligence (pp. 431-440).

Bibtex citation

@inproceedings{NodKolShe05,
   author = "Uri Nodelman and Daphne Koller and Christian R. Shelton",
   title = "Expectation Propagation for Continuous Time {B}ayesian Networks",
   booktitle = "Proceedings of the Twenty-First International Conference on Uncertainty in Artificial Intelligence",
   booktitleabbr = "{UAI}",
   year = 2005,
   pages = "431--440",
}