Error estimation in approximate Bayesian belief network inference

Authors:
Enrique F. Castillo;Remco R. Bouckaert;José M. Sarabia;Cristina Solares
Affiliations:
Applied Mathematics Dept., University of Cantabria, Santander, Spain;Computer Science Dept., Utrecht University, The Netherlands;Economics Dept., University of Cantabria, Santander, Spain;Applied Mathematics Dept., University of Cantabria, Santander, Spain
Venue:
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Year:
1995

Citing 3
Cited 0

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Abstract

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.