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.
This research was supported by an HKBU FRG grant 96–97/II-67.
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Hickernell, F.J. (1998). Lattice Rules: How Well Do They Measure Up?. In: Hellekalek, P., Larcher, G. (eds) Random and Quasi-Random Point Sets. Lecture Notes in Statistics, vol 138. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1702-2_3
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DOI: https://doi.org/10.1007/978-1-4612-1702-2_3
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