Name
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. |
Year | Venue | Keywords |
|---|---|---|
2013 | UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence | bayesian belief network |
DocType | Volume | ISBN |
Journal | abs/1302.4934 | 1-55860-385-9 |
Citations | PageRank | References |
2 | 0.51 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrique Castillo | 1 | 555 | 59.86 |
| Remco R. Bouckaert | 2 | 484 | 82.93 |
| José María Sarabia | 3 | 36 | 7.61 |
| Cristina Solares | 4 | 46 | 7.89 |