Markov's Inequality states that for a non-negative random variable, the probability that it exceeds a positive value a is at most the expected value of the random variable divided by a. Named for Andrey Markov, it is one of the most basic tail bounds in probability theory and is used in turn to derive Chebyshev's Inequality.
Facts
StatementMarkov's inequality states that for a nonnegative random variable X and any positive constant a, the probability that X is at least a is at most the expected value of X divided by a. 1 Classification
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Derived from the theorem's own name (unambiguous possessive-token match to exactly one live mathematician entity, w-bfill-g5-0924 browse backfill)
Sources
1. Markov's Inequality (Wikipedia)
Wikimedia Foundationlead section, first paragraphQuote, lead section, first paragraph
In probability theory, Markov's inequality gives an upper bound on the probability that a non-negative random variable is greater than or equal to some positive constant.
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