- Abstract:
- 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:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
-
-
(pdf, 805.5KB)
-
- Publisher copy:
- 10.1137/140960086
-
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- Publisher:
- Society for Industrial and Applied Mathematics Publisher's website
- Journal:
- SIAM/ASA Journal on Uncertainty Quantification Journal website
- Volume:
- 3
- Issue:
- 1
- Pages:
- 267–295
- Publication date:
- 2015-01-01
- DOI:
- EISSN:
-
2166-2525
- URN:
-
uuid:d0dd57ee-5116-4e79-ba71-f47c55075598
- Source identifiers:
-
508957
- Local pid:
- pubs:508957
- Copyright holder:
- Society for Industrial and Applied Mathematics and American Statistical Association
- Copyright date:
- 2015
- Notes:
- 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.
Journal article
Multi-level Monte Carlo approximation of distribution functions and densities
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