Abstract
This article deals with the possibility of using Asymptotic Sampling (AS) for the estimation of failure probability. The AS algorithm requires samples of multidimensional Gaussian random vectors. There are many alternative means of obtaining such samples and the selected sampling strategy influences the performance of the AS method. Several reliability problems (testing functions) have been selected in order to test AS with various sampling schemes. First, the functions are analysed using AS in combination with (i) Monte Carlo designs, (ii) LHS designs optimized using the Periodic Audze-Eglājs (PAE) criterion and, (iii) designs prepared using Sobol sequences. Afterwards, the same set of problems is solved without utilizing the AS procedure. This is achieved via the direct estimation of failure probability. All the results are also compared with the exact failure probability value.
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Acknowledgments
The authors acknowledge financial support provided by the Czech Ministry of Education, Youth and Sports under project no. FAST-J-16-3194 and also support provided by the Czech Science Foundation under project no. GA16-22230S.
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Šmídová, M., Vořechovský, M. (2017). Performance of Various Sampling Schemes in Asymptotic Sampling. In: Caspeele, R., Taerwe, L., Proske, D. (eds) 14th International Probabilistic Workshop . Springer, Cham. https://doi.org/10.1007/978-3-319-47886-9_4
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DOI: https://doi.org/10.1007/978-3-319-47886-9_4
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