Sampling algorithms for estimating the mean of bounded random variables
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We show two distribution-independent algorithms to estimate the mean of bounded random variables, one with the knowledge of variance, the other without. These algorithms guarantee that the estimate is within the desired precision with an error probability less than or equal to the requirement. Some simplified stopping rules are also given.
KeywordsAlgorithm Sampling Monte Carlo estimation Confidence intervals
I would like to thank the anonymous reviewers and Marek J. Druzdzel for their useful comments that considerably improved the presentation of this paper.
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