Linked from

The 10 pages that link to Chernoff bound, each with the reason it gives.

• Jensen's inequalityRelated: Convexity and expectation inequalities help convert exponential moments into tail bounds.

• Moment-generating functionRelated: Evaluating the function at a positive argument yields bounds on upper-tail probabilities.

• Markov's inequalityRelated: It strengthens basic expectation bounds when exponential moments are available.

• Chebyshev's inequalityCompared with: With stronger distributional information, it often gives exponentially tighter tails than Chebyshev's bound.

• Large deviation theoryRelated: It is a basic tool for deriving exponential estimates before sharper asymptotics.

• Concentration inequalityBroader topic: Exponential moments often yield sharp bounds for sums of independent variables.

• Hoeffding's inequalityRelated: Hoeffding’s inequality is obtained by optimizing a Chernoff bound.

• Condorcet's jury theoremRelated: It quantifies how quickly the chance of a mistaken majority can shrink.

• Azuma's inequalityRelated: Azuma's proof uses the same exponential-moment strategy, adapted to dependent increments.

• Boole's inequalityRelated: Probability proofs often combine concentration bounds with Boole's inequality across many events.