In probability theory, Kolmogorov's inequality is a so-called "maximal inequality" that gives a bound on the probability that the partial sums of a finite collection of independent random variables exceed some specified bound. The inequality is named after the Russian mathematician Andrey Kolmogorov.
Statement of the inequality
Let X1, ..., Xn : Ω → R be independent random variables defined on a common probability space (Ω, F, Pr), with expected value E[Xk] = 0 and variance Var[Xk] < +∞ for k = 1, ..., n. Then, for each λ > 0,
where Sk = X1 + ... + Xk.
The convenience of this result is that we can bound the worst case deviation of a random walk at any point of time using its value at the end of time interval.
The following argument is due to Kareem Amin and employs discrete martingales.
As argued in the discussion of Doob's martingale inequality, the sequence is a martingale.
Without loss of generality, we can assume that and for all .
Define as follows. Let , and
for all .
Then is also a martingale. Since is independent and mean zero,
The same is true for . Thus
by Chebyshev's inequality.
This inequality was generalized by Hájek and Rényi in 1955.
- Billingsley, Patrick (1995). Probability and Measure. New York: John Wiley & Sons, Inc. ISBN 0-471-00710-2. (Theorem 22.4)
- Feller, William (1968) . An Introduction to Probability Theory and its Applications, Vol 1 (Third ed.). New York: John Wiley & Sons, Inc. xviii+509. ISBN 0-471-25708-7.
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