Multilevel Monte Carlo metamodeling
Pages 509 - 520
Abstract
Multilevel Monte Carlo (MLMC) methods have been used by the information-based complexity community in order to improve the computational efficiency of parametric integration. We extend this approach by relaxing the assumptions on differentiability of the simulation output. Relaxing the assumption on the differentiability of the simulation output makes the MLMC method more widely applicable to stochastic simulation metamodeling problems in industrial engineering. The proposed scheme uses a sequential experiment design which allocates effort unevenly among design points in order to increase its efficiency. The procedure's efficiency is tested on an example of option pricing in the Black-Scholes model.
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- Multilevel Monte Carlo metamodeling
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Multilevel Monte Carlo Metamodeling
Approximating the function that maps the input parameters of the simulation model to the expectation of the simulation output is an important and challenging problem in stochastic simulation metamodeling. Because an expectation is an integral, this ...
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Information
Published In
December 2013
4386 pages
ISBN:9781479920778
- General Chair:
- Raymond R. Hill,
- Program Chair:
- Michael E. Kuhl
Sponsors
- IIE: Institute of Industrial Engineers
- INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
- ASA: American Statistical Association
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- SCS: Society for Modeling and Simulation International
- ASIM: Arbeitsgemeinschaft Simulation
- IEEE/SMCS: Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
- NIST: National Institute of Standards & Technology
Publisher
IEEE Press
Publication History
Published: 08 December 2013
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- Research-article
Conference
WSC '13
Sponsor:
- IIE
- INFORMS-SIM
- ASA
- SIGSIM
- SCS
- ASIM
- IEEE/SMCS
- NIST
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Overall Acceptance Rate 3,413 of 5,075 submissions, 67%
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