Uncertainty in Artificial Intelligence
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An Importance Sampling Algorithm Based on Evidence Pre-propagation
Changhe Yuan, Marek Druzdzel
Abstract:
Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Evidence Pre-propagation Importance Sampling algorithm (EPIS-BN), an importance sampling algorithm that computes an approximate importance function by the heuristic methods: loopy belief Propagation and e-cutoff. We tested the performance of e-cutoff on three large real Bayesian networks: ANDES, CPCS, and PATHFINDER. We observed that on each of these networks the EPIS-BN algorithm gives us a considerable improvement over the current state of the art algorithm, the AIS-BN algorithm. In addition, it avoids the costly learning stage of the AIS-BN algorithm.
Keywords: Importance Sampling Algorithm Based Evidence Pre-propagation
Pages: 624-631
PS Link: null
PDF Link: /papers/03/p624-yuan.pdf
BibTex:
@INPROCEEDINGS{Yuan03,
AUTHOR = "Changhe Yuan and Marek Druzdzel",
TITLE = "An Importance Sampling Algorithm Based on Evidence Pre-propagation",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "624--631"
}


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