(Enter summary)
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... (Update)
Context of citations to this paper: More
.... Work that also refer to lower bounds on query complexity for approximate solutions include results on the approximation of the mean [26], 34, 80] the approximation on the frequency moment [29] 8 Lossy compression may be related to sampling. When we have les in...
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BibTeX entry: (Update)
R. Canetti, G. Even and O. Goldreich, "Lower Bounds for Sampling Algorithms for Estimating the Average", IPL, Vol. 53, pp. 17--25, 1995. http://citeseer.ist.psu.edu/canetti94lower.html More
@article{ canetti95lower,
author = "Ran Canetti and Guy Even and Oded Goldreich",
title = "Lower Bounds for Sampling Algorithms for Estimating the Average",
journal = "Information Processing Letters",
volume = "53",
number = "1",
pages = "17-25",
year = "1995",
url = "citeseer.ist.psu.edu/canetti94lower.html" }
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