Corpus ID: 19003484

Expectation Propagation for Continuous Time Bayesian Networks

@inproceedings{Nodelman2005ExpectationPF,
  title={Expectation Propagation for Continuous Time Bayesian Networks},
  author={U. Nodelman and D. Koller and C. Shelton},
  booktitle={UAI},
  year={2005}
}
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… Expand
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