A stochastic algorithm for high-dimensional integrals over unbounded regions with Gaussian weight. The paper presents an algorithm that uses stochastic spherical-radial rules for the numerical computation of multiple integrals. These rules have higher accuracy and better convergence properties than simple Monte Carlo methods. The Fortran implemetation of the algorithm (RANRTH) is discussed, too. An example from a computational finance application is included (n variables, n>100).
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Ross, Andrew M.: Computing bounds on the expected maximum of correlated normal variables (2010)
- Huang, Bin; Li, Qiu Sheng; Tuan, Alex Y.; Zhu, Hongping: Recursive approach for random response analysis using non-orthogonal polynomial expansion (2009)
- Evans, Michael; Swartz, Tim: Approximating integrals via Monte Carlo and deterministic methods (2000)
- Genz, Alan; Monahan, John: A stochastic algorithm for high-dimensional integrals over unbounded regions with Gaussian weight (1999)