Name
Abstract | ||
|---|---|---|
Computing failure probability is a fundamental task in many important practical problems. The computation, its numerical challenges aside, naturally requires knowledge of the probability distribution of the underlying random inputs. On the other hand, for many complex systems it is often not possible to have complete information about the probability distributions. In such cases the uncertainty is often referred to as epistemic uncertainty, and straightforward computation of the failure probability is not available. In this paper we develop a method to estimate both the upper bound and the lower bound of the failure probability subject to epistemic uncertainty. The bounds are rigorously derived using the variational formulas for relative entropy. We examine in detail the properties of the bounds and present numerical algorithms to efficiently compute them. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1137/120864155 | SIAM JOURNAL ON SCIENTIFIC COMPUTING |
Keywords | Field | DocType |
failure probability,uncertainty quantification,epistemic uncertainty,relative entropy | Probability and statistics,Probability box,Mathematical optimization,Applied probability,Uncertainty analysis,Empirical probability,Probability distribution,Probability bounds analysis,Kullback–Leibler divergence,Mathematics | Journal |
Volume | Issue | ISSN |
34 | 6 | 1064-8275 |
Citations | PageRank | References |
7 | 0.71 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Li | 1 | 99 | 6.73 |
| Dongbin Xiu | 2 | 1068 | 115.57 |