Krumscheid, S.; Nobile, F.; Pisaroni, M. Quantifying uncertain system outputs via the multilevel Monte Carlo method. I: Central moment estimation. (English) Zbl 1440.65008 J. Comput. Phys. 414, Article ID 109466, 20 p. (2020). Summary: In this work we introduce and analyze a novel multilevel Monte Carlo (MLMC) estimator for the accurate approximation of central moments of system outputs affected by uncertainties. Central moments play a central role in many disciplines to characterize a random system output’s distribution and are of primary importance in many prediction, optimization, and decision making processes under uncertainties. We detail how to effectively tune the MLMC algorithm for central moments of any order and present a complete practical algorithm that is implemented as part of a Python library [R. Amela et al., ExaQUte XMC, doi:10.5281/zenodo.3235833]. 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