Abstract
The First Moment Principle states that a random variable X is at most E(X) with positive probability. Often we require that X is near E(X) with very high probability. When this is the case, we say that X is concentrated. In this book, we will see a number of tools for proving that a random variable is concentrated, including Talagrand’s Inequality and Azuma’s Inequality. In this chapter, we begin with the simplest such tool, the Chernoff Bound.
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© 2002 Springer-Verlag Berlin Heidelberg
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Molloy, M., Reed, B. (2002). The Chernoff Bound. In: Graph Colouring and the Probabilistic Method. Algorithms and Combinatorics, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04016-0_5
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DOI: https://doi.org/10.1007/978-3-642-04016-0_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04015-3
Online ISBN: 978-3-642-04016-0
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