We show how information on the uniformity properties of a point set employed in numerical multi-dimensional integration can be used to improve the error estimate over the usual Monte Carlo one. We introduce a new measure of (non)uniformity for point sets, and derive explicit expressions for the vari
β¦ LIBER β¦
Discrepancy-based error estimates for Quasi-Monte Carlo III. Error distributions and central limits
β Scribed by Jiri Hoogland; Ronald Kleiss
- Book ID
- 108314531
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 648 KB
- Volume
- 101
- Category
- Article
- ISSN
- 0010-4655
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Discrepancy-based error estimates for Qu
β
Jiri K. Hoogland; Ronald Kleiss
π
Article
π
1996
π
Elsevier Science
π
English
β 852 KB
Discrepancy-based error estimates for Qu
β
Jiri K. Hoogland; Ronald Kleiss
π
Article
π
1996
π
Elsevier Science
π
English
β 410 KB
The choice of a point set, to be used in numerical integration, determines, to a large extent, the error estimate of the integral. Point sets can be characterized by their discrepancy, which is a measure of their nonuniformity. Point sets with a discrepancy that is low with respect to the expected v
A central limit theorem and improved err
β
Giray Γkten; Bruno Tuffin; Vadim Burago
π
Article
π
2006
π
Elsevier Science
π
English
β 256 KB