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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Extrapolation, Invariance, Geometry and Subset Sampling
verfasst von : K. Breitung
Erschienen in: 14th International Probabilistic Workshop
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Abstract
In the last years the subset sampling method has often been used in reliability problems as a tool for calculating very small probabilities. The method extrapolates from an initial Monte Carlo estimate for the probability content of a failure domain found by a suitable higher level of the original limit state function. Then iteratively conditional probabilities are estimated for values of the limit state function decreasing to zero. But there are implicit assumptions about the structure of the failure domains which have to be fulfilled that the method works properly. It is shown by examples that at least in some cases if these assumptions are not fulfilled, erroneous results may be obtained. For the further development of the subset sampling concept it might be desirable to find approaches where it is possible to ascertain that these implicit assumptions are not violated or how to avoid by an increased computational effort misleading influences of the structure of the limit state functions.