On NonAsymptotic Optimal Stopping Criteria in Monte Carlo Simulations
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/555670
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AbstractWe consider the setting of estimating the mean of a random variable by a sequential stopping rule Monte Carlo (MC) method. The performance of a typical second moment based sequential stopping rule MC method is shown to be unreliable in such settings both by numerical examples and through analysis. By analysis and approximations, we construct a higher moment based stopping rule which is shown in numerical examples to perform more reliably and only slightly less efficiently than the second moment based stopping rule.
CitationOn NonAsymptotic Optimal Stopping Criteria in Monte Carlo Simulations 2014, 36 (2):A869 SIAM Journal on Scientific Computing