Uncertainty in Artificial Intelligence
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Error Estimation in Approximate Bayesian Belief Network Inference
Enrique Castillo, Remco Bouckaert, Jose Sarabia, Cristina Solares
We can perform inference in Bayesian belief networks by enumerating instantiations with high probability thus approximating the marginals. In this paper, we present a method for determining the fraction of instantiations that has to be considered such that the absolute error in the marginals does not exceed a predefined value. The method is based on extreme value theory. Essentially, the proposed method uses the reversed generalized Pareto distribution to model probabilities of instantiations below a given threshold. Based on this distribution, an estimate of the maximal absolute error if instantiations with probability smaller than u are disregarded can be made.
Keywords: Approximate inference, Bayesian networks, extremes, error bounds.
Pages: 55-62
PS Link:
PDF Link: /papers/95/p55-castillo.pdf
AUTHOR = "Enrique Castillo and Remco Bouckaert and Jose Sarabia and Cristina Solares",
TITLE = "Error Estimation in Approximate Bayesian Belief Network Inference",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "55--62"

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