Chernoff bound is an exponentially decreasing upper bound on the tail of a random variable based on its moment generating function. It is sharper than other bounds such as Markov's and Chebyshev's inequalities, but requires the variates to be independent. It is related to Bernstein inequalities and used to prove Hoeffding's, Bennett's and McDiarmid's inequalities.
UC Berkeley
Fall 2013
This course investigates the mathematical principles behind data and information analysis. It brings together concepts from statistics, optimization, and computer science, with a focus on large deviation inequalities, and convex analysis. It's tailored towards advanced graduate students who wish to incorporate these theories into their research.
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