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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...

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Detalles Bibliográficos
Autores principales: James, F., Hoogland, J., Kleiss, R.
Lenguaje:eng
Publicado: 1996
Materias:
Acceso en línea:https://dx.doi.org/10.1016/S0010-4655(96)00108-7
http://cds.cern.ch/record/305066
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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.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 1996
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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
work_keys_str_mv AT jamesf multidimensionalsamplingforsimulationandintegrationmeasuresdiscrepanciesandquasirandomnumbers
AT hooglandj multidimensionalsamplingforsimulationandintegrationmeasuresdiscrepanciesandquasirandomnumbers
AT kleissr multidimensionalsamplingforsimulationandintegrationmeasuresdiscrepanciesandquasirandomnumbers