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Multi-level Monte Carlo approximation of distribution functions and densities

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

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Publisher copy:
10.1137/140960086

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Institution:
University of Oxford
Department:
Oxford, MPLS, Mathematical Inst
Role:
Author
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

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