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[1] [2] The term Chebyshev's inequality may also refer to Markov's inequality, especially in the context of analysis. They are closely related, and some authors refer to Markov's inequality as "Chebyshev's First Inequality," and the similar one referred to on this page as "Chebyshev's Second Inequality."
Bound the desired probability using the Chebyshev inequality: ... "8.4 The coupon collector's problem solved", The Pleasures of Probability, Undergraduate Texts ...
5.1 Proof using Chebyshev's inequality assuming finite variance. ... the law of large numbers does not help in solving the bias. Even if the number of trials is ...
In mathematical analysis, the Chebyshev–Markov–Stieltjes inequalities are inequalities related to the problem of moments that were formulated in the 1880s by Pafnuty Chebyshev and proved independently by Andrey Markov and (somewhat later) by Thomas Jan Stieltjes. [1]
In mathematics, Chebyshev's sum inequality, named after Pafnuty Chebyshev, states that if ...
In probability theory, the multidimensional Chebyshev's inequality [1] is a generalization of Chebyshev's inequality, which puts a bound on the probability of the event that a random variable differs from its expected value by more than a specified amount.
Such inequalities are of importance in several fields, including communication complexity (e.g., in proofs of the gap Hamming problem [13]) and graph theory. [14] An interesting anti-concentration inequality for weighted sums of independent Rademacher random variables can be obtained using the Paley–Zygmund and the Khintchine inequalities. [15]
Chebyshev's equation is the second order linear differential equation ... This page was last edited on 7 August 2022, at 12:25 (UTC).