The Chernoff Bound is a probabilistic inequality that provides exponentially decreasing bounds on the tail distributions of sums of independent random variables. It is especially useful in analyzing the probability of large deviations from the expected value, which ties into the principles of large deviation theory. This bound allows for more precise estimates than simpler forms like Chebyshev's inequality, making it a powerful tool in areas like computer science, statistics, and information theory.
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