Multi-level Monte Carlo approximation of distribution functions and densities
- We construct and analyze multi-level Monte Carlo methods for the approximation of distribution functions and densities of univariate random variables. Since, by assumption, the target distribution is not known explicitly, approximations have to be used. We provide a general analysis under suitable assumptions on the weak and strong convergence. We apply the results to smooth path-independent and path-dependent functionals and to stopped exit times of SDEs.
- Publication status:
- Peer review status:
- Peer reviewed
(Accepted manuscript, pdf, 805.5KB)
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- Copyright holder:
- Society for Industrial and Applied Mathematics and American Statistical Association
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- Copyright © 2015 Society for Industrial and Applied Mathematics. This manuscript has been published in SIAM/ASA Journal on Uncertainty Quantification. Unauthorized reproduction of this article is prohibited.
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