Linked from

The 14 pages that link to Markov's inequality, each with the reason it gives.

• Jensen's inequalityRelated: Applying Jensen to an exponential moment helps derive exponential probability bounds.

• Convergence in probabilityRelated: It turns vanishing expected error measures into bounds on large-error probabilities.

• Probabilistic methodRelated: It converts an expectation bound into a probability bound on undesirable outcomes.

• Uniform integrabilityRelated: It converts integral bounds into control of the measure of large-value sets.

• Chebyshev's inequalityRelated: Applying it to squared deviations from the mean gives Chebyshev's bound.

• Chernoff boundRelated: Applying it to an exponential transform produces the Chernoff bound.

• Andrey MarkovRelated: This separate probability result also carries Markov’s name and reflects his broader mathematical work.

• Upper boundRelated: It bounds tail probabilities using only a variable's expectation.

• Concentration inequalityBroader topic: It gives a basic tail bound from only nonnegativity and the mean.

• Bernoulli's inequalityCompared with: It supplies a different route to quantitative bounds, from expectations rather than power expansions.

• Kolmogorov continuity theoremRelated: Moment estimates become tail bounds through Markov’s inequality.

• Kolmogorov's inequalityRelated: A second-moment argument applies this inequality after isolating the first crossing.

• Markov brothers' inequalityBroader topic: This is the first-derivative form at the heart of the brothers' inequality.

• Popoviciu's inequalityRelated: Applied to squared deviations, it converts the variance bound into a tail bound.