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Multilevel Monte Carlo methods in numerical analysis are algorithms for computing expectations that arise in stochastic simulations. Just as Monte Carlo methods, they rely on repeated random sampling, but these samples are taken on different levels of accuracy. MLMC methods can greatly reduce the computational cost of standard Monte Carlo methods by taking most samples with a low accuracy and corresponding low cost, and only very few samples are taken at high accuracy and corresponding high cost.
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