@inbook{db2fc5c05f6e4744aa21a08ff0ac0477,
title = "Lattice Rules: How Well Do They Measure Up?",
abstract = "A simple, but often effective, way to approximate an integral over the s-dimensional unit cube is to take the average of the integrand over some set P of N points. Monte Carlo methods choose P randomly and typically obtain an error of 0(N-1/2). Quasi-Monte Carlo methods attempt to decrease the error by choosing P in a deterministic (or quasi-random) way so that the points are more uniformly spread over the integration domain.",
keywords = "Quadrature Rule, Reproduce Kernel Hilbert Space, Star Discrepancy, Quadrature Error, Bernoulli Polynomial",
author = "Hickernell, {Fred J.}",
note = "Funding Information: This research was supported by an HKBU FRG grant 96–97/II-67. Publisher copyright: {\textcopyright} 1998 Springer Science+Business Media New York",
year = "1998",
month = oct,
day = "9",
doi = "10.1007/978-1-4612-1702-2_3",
language = "English",
isbn = "9780387985541",
series = "Lecture Notes in Statistics",
publisher = "Springer",
pages = "109–166",
editor = "Peter Hellekalek and Gerhard Larcher",
booktitle = "Random and Quasi-Random Point Sets",
edition = "1st",
}