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R-LAIR: Riverside Lab for Artificial Intelligence Research

Sampling for Approximate Inference in Continuous Time Bayesian Networks (2008)

by Yu Fan and Christian R. Shelton

Abstract: We first present a sampling algorithm for continuous time Bayesian networks based on importance sampling. We then extend it to continuous-time particle filtering and smoothing algorithms. The three algorithms can estimate the expectation of any function of a trajectory, conditioned on any evidence set constraining the values of subsets of the variables over subsets of the timeline. We present experimental results on their accuracies and time efficiencies, and compare them to expectation propagation.

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Yu Fan and Christian R. Shelton (2008). "Sampling for Approximate Inference in Continuous Time Bayesian Networks." Tenth International Symposium on Artificial Intelligence and Mathematics. pdf ps    

Bibtex citation

@inproceedings{FanShe08,
   author = "Yu Fan and Christian R. Shelton",
   title = "Sampling for Approximate Inference in Continuous Time {B}ayesian Networks",
   booktitle = "Tenth International Symposium on Artificial Intelligence and Mathematics",
   booktitleabbr = "{AIM}-2008",
   year = 2008,
}

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