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R-LAIR: Riverside Lab for Artificial Intelligence Research

Factored Filtering of Continuous-Time Systems (2011)

by E. Busra Celikkaya, Christian R. Shelton, and William Lam

Abstract: We consider filtering for a continuous-time, or asynchronous, stochastic system where the full distribution over states is too large to be stored or calculated. We assume that the rate matrix of the system can be compactly represented and that the belief distribution is to be approximated as a product of marginals. The essential computation is the matrix exponential. We look at two different methods for its computation: ODE integration and uniformization of the Taylor expansion. For both we consider approximations in which only a factored belief state is maintained. For factored uniformization we demonstrate that the KL-divergence of the filtering is bounded. Our experimental results confirm our factored uniformization performs better than previously suggested uniformization methods and the mean field algorithm.

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E. Busra Celikkaya, Christian R. Shelton, and William Lam (2011). "Factored Filtering of Continuous-Time Systems." Proceedings of the Twenty-Seventh International Conference on Uncertainty in Artificial Intelligence. pdf      

Bibtex citation

@inproceedings{CelSheLam11,
   author = "E. Busra Celikkaya and Christian R. Shelton and William Lam",
   title = "Factored Filtering of Continuous-Time Systems",
   booktitle = "Proceedings of the Twenty-Seventh International Conference on Uncertainty in Artificial Intelligence",
   booktitleabbr = "{UAI}-2011",
   year = 2011
}

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