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arXiv · hep-ph/9603211

Discrepancy-based error estimates for Quasi-Monte Carlo. II: Results in one dimension

Jiri Hoogland·Ronald Kleiss
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Abstract

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 its non-uniformity. Point sets with a discrepancy that is low with respect to the expected value for truly random point sets, are generally thought to be desirable. A low value of the discrepancy implies a negative correlation between the points, which may be usefully employed to improve the error estimate of a numerical integral based on the point set. We apply the formalism developed in a previous publication to compute this correlation for one-dimensional point sets, using a few different definitions of discrepancy.

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Jiri Hoogland, Ronald Kleiss. 1996-03-02. Discrepancy-based error estimates for Quasi-Monte Carlo. II: Results in one dimension. https://doi.org/10.1016/0010-4655(96)00083-5

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