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• W3121872506 abstract "This work introduces two new techniques for random number generation with any prescribed nonlinear distribution based on the k-vector methodology. The first approach is based on inverse transform sampling using the optimal k-vector to generate the samples by inverting the cumulative distribution. The second approach generates samples by performing random searches in a pre-generated large database previously built by massive inversion of the prescribed nonlinear distribution using the k-vector. Both methods are shown suitable for massive generation of random samples. Examples are provided to clarify these methodologies." @default.
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• W3121872506 date "2019-01-01" @default.
• W3121872506 modified "2023-09-26" @default.
• W3121872506 title "Random Sampling Using -Vector" @default.
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• W3121872506 doi "https://doi.org/10.1109/mcse.2018.2882727" @default.
• W3121872506 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7268911" @default.
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