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Multidimensional sampling for simulation and integration: measures, discrepancies, and quasi-random numbers
This is basically a review of the field of Quasi-Monte Carlo intended for computational physicists and other potential users of quasi-random numbers. As such, much of the material is not new, but is presented here in a style hopefully more accessible to physicists than the specialized mathematical l...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
1996
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Acceso en línea: | https://dx.doi.org/10.1016/S0010-4655(96)00108-7 http://cds.cern.ch/record/305066 |
_version_ | 1780889755298299904 |
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author | James, F. Hoogland, J. Kleiss, R. |
author_facet | James, F. Hoogland, J. Kleiss, R. |
author_sort | James, F. |
collection | CERN |
description | This is basically a review of the field of Quasi-Monte Carlo intended for computational physicists and other potential users of quasi-random numbers. As such, much of the material is not new, but is presented here in a style hopefully more accessible to physicists than the specialized mathematical literature. There are also some new results: On the practical side we give important empirical properties of large quasi-random point sets, especially the exact quadratic discrepancies; on the theoretical side, there is the exact distribution of quadratic discrepancy for random point sets. |
id | cern-305066 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 1996 |
record_format | invenio |
spelling | cern-3050662023-03-12T05:56:48Zdoi:10.1016/S0010-4655(96)00108-7http://cds.cern.ch/record/305066engJames, F.Hoogland, J.Kleiss, R.Multidimensional sampling for simulation and integration: measures, discrepancies, and quasi-random numbersParticle Physics - PhenomenologyThis is basically a review of the field of Quasi-Monte Carlo intended for computational physicists and other potential users of quasi-random numbers. As such, much of the material is not new, but is presented here in a style hopefully more accessible to physicists than the specialized mathematical literature. There are also some new results: On the practical side we give important empirical properties of large quasi-random point sets, especially the exact quadratic discrepancies; on the theoretical side, there is the exact distribution of quadratic discrepancy for random point sets.This is basically a review of the field of Quasi-Monte Carlo intended for computational physicists and other potential users of quasi-random numbers. As such, much of the material is not new, but is presented here in a style hopefully more accessible to physicists than the specialized mathematical literature. There are also some new results: On the practical side we give important empirical properties of large quasi-random point sets, especially the exact quadratic discrepancies; on the theoretical side, there is the exact distribution of quadratic discrepancy for random point sets.This is basically a review of the field of Quasi-Monte Carlo intended for computational physicists and other potential users of quasi-random numbers. As such, much of the material is not new, but is presented here in a style hopefully more accessible to physicists than the specialized mathematical literature. There are also some new results: On the practical side we give important empirical properties of large quasi-random point sets, especially the exact quadratic discrepancies; on the theoretical side, there is the exact distribution of quadratic discrepancy for random point sets.This is basically a review of the field of Quasi-Monte Carlo intended for computational physicists and other potential users of quasi-random numbers. As such, much of the material is not new, but is presented here in a style hopefully more accessible to physicists than the specialized mathematical literature. There are also some new results: On the practical side we give important empirical properties of large quasi-random point sets, especially the exact quadratic discrepancies; on the theoretical side, there is the exact distribution of quadratic discrepancy for random point sets.This is basically a review of the field of Quasi-Monte Carlo intended for computational physicists and other potential users of quasi-random numbers. As such, much of the material is not new, but is presented here in a style hopefully more accessible to physicists than the specialized mathematical literature. There are also some new results: On the practical side we give important empirical properties of large quasi-random point sets, especially the exact quadratic discrepancies; on the theoretical side, there is the exact distribution of quadratic discrepancy for random point sets.hep-ph/9606309NIKHEF-96-017NIKHEF-96-017oai:cds.cern.ch:3050661996-06-12 |
spellingShingle | Particle Physics - Phenomenology James, F. Hoogland, J. Kleiss, R. Multidimensional sampling for simulation and integration: measures, discrepancies, and quasi-random numbers |
title | Multidimensional sampling for simulation and integration: measures, discrepancies, and quasi-random numbers |
title_full | Multidimensional sampling for simulation and integration: measures, discrepancies, and quasi-random numbers |
title_fullStr | Multidimensional sampling for simulation and integration: measures, discrepancies, and quasi-random numbers |
title_full_unstemmed | Multidimensional sampling for simulation and integration: measures, discrepancies, and quasi-random numbers |
title_short | Multidimensional sampling for simulation and integration: measures, discrepancies, and quasi-random numbers |
title_sort | multidimensional sampling for simulation and integration: measures, discrepancies, and quasi-random numbers |
topic | Particle Physics - Phenomenology |
url | https://dx.doi.org/10.1016/S0010-4655(96)00108-7 http://cds.cern.ch/record/305066 |
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