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Lower bounds for sampling algorithms for estimating the average. (English) Zbl 0875.68529
Summary: We show lower bounds on the number of sample points and on the number of coin tosses used by general sampling algorithms for estimating the average value of functions over a large domain. The bounds depend on the desired precision and on the error probability of the estimate. Our lower bounds match upper bounds established by known algorithms, up to a multiplicative constant. Furthermore, we give a non-constructive proof of existence of an algorithm that improves the known upper bounds by a constant factor.

MSC:
68Q25 Analysis of algorithms and problem complexity
65C05 Monte Carlo methods
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