Error Estimation in Approximate Bayesian Belief Network Inference
  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.
