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Random Sampling Using -Vector

This paper introduces two new techniques for random number generation with any prescribed nonlinear distribution based on the [Formula Omitted]-vector methodology. The first approach is based on an inverse transform sampling using the optimal [Formula Omitted]-vector to generate the samples by inver...

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Bibliographic Details
Published in:Computing in science & engineering 2019-01, Vol.21 (1), p.94-107
Main Authors: Arnas, David, Leake, Carl, Mortari, Daniele
Format: Article
Language:English
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Summary:This paper introduces two new techniques for random number generation with any prescribed nonlinear distribution based on the [Formula Omitted]-vector methodology. The first approach is based on an inverse transform sampling using the optimal [Formula Omitted]-vector to generate the samples by inverting the cumulative distribution. The second approach generates samples by performing random searches in a pregenerated large database previously built by massive inversion of the prescribed nonlinear distribution using the [Formula Omitted]-vector. Both methods are shown to be suitable for massive generation of random samples. Examples are provided to clarify these methodologies.
ISSN:1521-9615
1558-366X
DOI:10.1109/MCSE.2018.2882727