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arXiv · 1209.6396

Chernoff-Hoeffding Inequality and Applications

Jeff M. Phillips
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Abstract

When dealing with modern big data sets, a very common theme is reducing the set through a random process. These generally work by making "many simple estimates" of the full data set, and then judging them as a whole. Perhaps magically, these "many simple estimates" can provide a very accurate and small representation of the large data set. The key tool in showing how many of these simple estimates are needed for a fixed accuracy trade-off is the Chernoff-Hoeffding inequality[Che52,Hoe63]. This document provides a simple form of this bound, and two examples of its use.

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Jeff M. Phillips. 2013-02-19. Chernoff-Hoeffding Inequality and Applications. https://arxiv.org/abs/1209.6396

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