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• W2107062904 abstract "We p ropose a statistical samp ling method, called eXtreme Event Sampling (XES), to co mpute far quantiles of arbitrary responses of multip le independent random parameters more accurately and efficiently than with Classical Monte-Carlo (CM C). Based on the selective over-samp ling o f events of low probability of occurrence, the method enables the study of mult iple responses at a time, unlike the classical Importance Sampling (IS) techniques, expected to be the best-performing when tuned to a single given response. Though more generic than IS, XES still shows large gains over CMC in both accuracy and sampling efficiency, even for a large nu mber of parameters (up to 27 tested). This art icle presents the detailed theoretical aspects of XES and an emp irical study demonstrating its efficiency in various cases of responses (linear or non-linear), distributions (normal and non-normal) and nu mber of parameters. If the primary target application is the design of semiconductor memo ry circuits, we believe that the flexib ility of the method potentially makes it attractive in other contexts showing similar constraints." @default.
• W2107062904 created "2016-06-24" @default.
• W2107062904 creator A5034050532 @default.
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• W2107062904 date "2012-01-01" @default.
• W2107062904 modified "2023-09-27" @default.
• W2107062904 title "A Multivariate Statistical Sampling Technique to Enhance Far Quantile Estimates of Arbitrary Responses" @default.
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