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Multi-index Monte Carlo: when sparsity meets sampling

Abstract:

We propose and analyze a novel multi-index Monte Carlo (MIMC) method for weak approximation of stochastic models that are described in terms of differential equations either driven by random measures or with random coefficients. The MIMC method is both a stochastic version of the combination technique introduced by Zenger, Griebel and collaborators and an extension of the multilevel Monte Carlo (MLMC) method first described by Heinrich and Giles. Inspired by Giles’s seminal work, we use in MI...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1007/s00211-015-0734-5

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Institution:
University of Oxford
Division:
MPLS Division
ORCID:
0000-0002-6243-0335
Tempone, R More by this author
Publisher:
Springer Publisher's website
Journal:
Numerische Mathematik Journal website
Publication date:
2015-06-27
Acceptance date:
2015-06-27
DOI:
EISSN:
0945-3245
ISSN:
0029-599X
Pubs id:
pubs:645310
URN:
uri:d857e6db-5e61-49a0-b90b-b577c32ac722
UUID:
uuid:d857e6db-5e61-49a0-b90b-b577c32ac722
Local pid:
pubs:645310
Keywords:

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