We discuss the problem of defining an estimate for the error in quasi-Monte Carlo integration. The key issue is the definition of an ensemble of quasi-random point sets that, on the one hand, includes a sufficiency of equivalent point sets, and on the other hand uses information on the degree of uniformity of the point set actually used, in the form of a discrepancy or diaphony. A few examples of such discrepancies are given. We derive the distribution of our error estimate in the limit of large number of points. In many cases, Gaussian central limits are obtained. We also present numerical results for the quadratic star-discrepancy for a number of quasi-random sequences
10.1016/S0377-0427(02)00665-9Journal of Computational and Applied Mathematics1502283-29
International audienceMonte Carlo is one of the most versatile and widely used numerical methods. It...
This talk gives an introduction to quasi-Monte Carlo methods for high-dimensional integrals. Such me...
We show how information on the uniformity properties of a point set employed in numerical multidimen...
This is basically a review of the field of Quasi-Monte Carlo intended for computational physicists a...
The author discusses the doubtful value of error-bounds and estimates of a statistical nature, based...
AbstractWe briefly discuss the following issues in quasi-Monte Carlo methods: error bounds and error...
Error estimation in Monte-Carlo integration is related to the star discrepancy of random point sets....
AbstractWe establish new error bounds for quasi-Monte Carlo integration for node sets with a special...
AbstractMeasures of irregularity of distribution, such as discrepancy and dispersion, play a major r...
The standard Monte Carlo approach to evaluating multi-dimensional integrals using (pseudo)-random in...
Randomized quasi-Monte Carlo methods have been introduced with the main purpose of yielding a comput...
The computation of integrals in higher dimensions and on general domains, when no explicit cubature...
AbstractRecently, quasi-Monte Carlo algorithms have been successfully used for multivariate integrat...
MCQMC2010Quasi-Monte Carlo methods can be used to approximate integrals in various weighted spaces o...
10.1016/S0377-0427(02)00665-9Journal of Computational and Applied Mathematics1502283-29
International audienceMonte Carlo is one of the most versatile and widely used numerical methods. It...
This talk gives an introduction to quasi-Monte Carlo methods for high-dimensional integrals. Such me...
We show how information on the uniformity properties of a point set employed in numerical multidimen...
This is basically a review of the field of Quasi-Monte Carlo intended for computational physicists a...
The author discusses the doubtful value of error-bounds and estimates of a statistical nature, based...
AbstractWe briefly discuss the following issues in quasi-Monte Carlo methods: error bounds and error...
Error estimation in Monte-Carlo integration is related to the star discrepancy of random point sets....
AbstractWe establish new error bounds for quasi-Monte Carlo integration for node sets with a special...
AbstractMeasures of irregularity of distribution, such as discrepancy and dispersion, play a major r...
The standard Monte Carlo approach to evaluating multi-dimensional integrals using (pseudo)-random in...
Randomized quasi-Monte Carlo methods have been introduced with the main purpose of yielding a comput...
The computation of integrals in higher dimensions and on general domains, when no explicit cubature...
AbstractRecently, quasi-Monte Carlo algorithms have been successfully used for multivariate integrat...
MCQMC2010Quasi-Monte Carlo methods can be used to approximate integrals in various weighted spaces o...
10.1016/S0377-0427(02)00665-9Journal of Computational and Applied Mathematics1502283-29
International audienceMonte Carlo is one of the most versatile and widely used numerical methods. It...
This talk gives an introduction to quasi-Monte Carlo methods for high-dimensional integrals. Such me...