## Abstract

A central task in many applications is reasoning about processes that change over continuous time. Continuous-Time Bayesian Networks is a general compact representation language for multi-component continuous-time processes. However, exact inference in such processes is exponential in the number of components, and thus infeasible for most models of interest. Here we develop a novel Gibbs sampling procedure for multi-component processes. This procedure iteratively samples a trajectory for one of the components given the remaining ones. We show how to perform exact sampling that adapts to the natural time scale of the sampled process. Moreover, we show that this sampling procedure naturally exploits the structure of the network to reduce the computational cost of each step. This procedure is the first that can provide asymptotically unbiased approximation in such processes.

Original language | American English |
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Title of host publication | Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008 |

Pages | 169-178 |

Number of pages | 10 |

State | Published - 2008 |

Event | 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008 - Helsinki, Finland Duration: 9 Jul 2008 → 12 Jul 2008 |

### Publication series

Name | Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008 |
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### Conference

Conference | 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008 |
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Country/Territory | Finland |

City | Helsinki |

Period | 9/07/08 → 12/07/08 |