A review of uncertainty quantification for quasi-Monte Carlo that recommends Student's t intervals from at least 10 randomized replicates and identifies near-symmetry of RQMC errors as a promising but unproven basis for confidence intervals.
A simple universal algorithm for high-dimensional integration
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abstract
We present a simple universal algorithm for high-dimensional integration which has the optimal error rate (independent of the dimension) in all weighted Korobov classes both in the randomized and the deterministic setting. Our theoretical findings are complemented by numerical tests.
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2024 1verdicts
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Error estimation for quasi-Monte Carlo
A review of uncertainty quantification for quasi-Monte Carlo that recommends Student's t intervals from at least 10 randomized replicates and identifies near-symmetry of RQMC errors as a promising but unproven basis for confidence intervals.