Lower bounds for sampling algorithms for estimating the average

Ran Canetti, Guy Even, Oded Goldreich*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)17-25
Number of pages9
JournalInformation Processing Letters
Volume53
Issue number1
DOIs
StatePublished - 13 Jan 1995
Externally publishedYes

Keywords

  • Estimating
  • Lower bounds
  • Randomness
  • Sampling
  • Theory of computation

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