Uncertainty in Artificial Intelligence
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Gibbs Sampling in Factorized Continuous-Time Markov Processes
Tal El-Hay, Nir Friedman, Raz Kupferman
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.
Keywords:
Pages: 169-178
PS Link:
PDF Link: /papers/08/p169-el-hay.pdf
BibTex:
@INPROCEEDINGS{El-Hay08,
AUTHOR = "Tal El-Hay and Nir Friedman and Raz Kupferman",
TITLE = "Gibbs Sampling in Factorized Continuous-Time Markov Processes",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "169--178"
}


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