Uncertainty in Artificial Intelligence
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Optimal Monte Carlo Estimation of Belief Network Inference
Malcolm Pradhan, Paul Dagum
Abstract:
We present two Monte Carlo sampling algorithms for probabilistic inference that guarantee polynomial-time convergence for a larger class of network than current sampling algorithms provide. These new methods are variants of the known likelihood weighting algorithm. We use of recent advances in the theory of optimal stopping rules for Monte Carlo simulation to obtain an inference approximation with relative error epsilon and a small failure probability delta. We present an empirical evaluation of the algorithms which demonstrates their improved performance.
Keywords: Belief network inference, Monte Carlo simulation, stopping rules.
Pages: 446-453
PS Link: http://www-ksl.stanford.edu/abstracts_by_author/Pradhan,M.papers.html
PDF Link: /papers/96/p446-pradhan.pdf
BibTex:
@INPROCEEDINGS{Pradhan96,
AUTHOR = "Malcolm Pradhan and Paul Dagum",
TITLE = "Optimal Monte Carlo Estimation of Belief Network Inference",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "446--453"
}


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