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- W2807940212 abstract "We survey basic ideas and results on randomized quasi-Monte Carlo (RQMC) methods, discuss their practical aspects, and give numerical illustrations. RQMC can improve accuracy compared with standard Monte Carlo (MC) when estimating an integral interpreted as a mathematical expectation. RQMC estimators are unbiased and their variance converges at a faster rate (under certain conditions) than MC estimators, as a function of the sample size. Variants of RQMC also work for the simulation of Markov chains, for function approximation and optimization, for solving partial differential equations, etc. In this introductory survey, we look at how RQMC point sets and sequences are constructed, how we measure their uniformity, why they can work for high-dimensional integrals, and how can they work when simulating Markov chains over a large number of steps." @default.
- W2807940212 created "2018-06-21" @default.
- W2807940212 creator A5091510633 @default.
- W2807940212 date "2018-01-01" @default.
- W2807940212 modified "2023-10-15" @default.
- W2807940212 title "Randomized Quasi-Monte Carlo: An Introduction for Practitioners" @default.
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- W2807940212 doi "https://doi.org/10.1007/978-3-319-91436-7_2" @default.
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