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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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Published in: | Computing in science & engineering 2019-01, Vol.21 (1), p.94-107 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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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. |
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ISSN: | 1521-9615 1558-366X |
DOI: | 10.1109/MCSE.2018.2882727 |