| TR#: | CS0789 |
| Class: | CS |
| Title: | LOWER BOUNDS FOR SAMPLING ALGORITHMS FOR ESTIMATING
THE AVERAGE. |
| Authors: | R. Canetti, G. Even and O. Goldreich |
| CS0789.pdf | |
| Abstract: |
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.
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