On NonAsymptotic Optimal Stopping Criteria in Monte Carlo Simulations

Christian Bayer, Hakon Hoel, Erik Von Schwerin, Raul Tempone

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


We 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.
Original languageEnglish (US)
Pages (from-to)A869-A885
Number of pages1
JournalSIAM Journal on Scientific Computing
Issue number2
StatePublished - Jan 2014

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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