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Kolmogorov's strong law of large numbers (Theorem)

Let $X_1,X_2,\dots$ be a sequence of independent random variables, with finite expectations. The strong law of large numbers holds if one of the following conditions is satisfied:

  1. The random variables are identically distributed;
  2. For each $n$ the variance of $X_n$ is finite, and $$\sum_{n=1}^\infty \frac{\operatorname{Var}[X_n]}{n^2} <\infty.$$




"Kolmogorov's strong law of large numbers" is owned by Koro.
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See Also: martingale proof of Kolmogorov's strong law for square integrable variables, proof of Kolmogorov's strong law for IID random variables

Other names:  Kolmogorov's criterion

Attachments:
martingale proof of Kolmogorov's strong law for square integrable variables (Proof) by gel
proof of Kolmogorov's strong law for IID random variables (Proof) by gel
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Cross-references: variance, identically distributed, strong law of large numbers, expectations, finite, random variables, independent, sequence
There are 2 references to this entry.

This is version 4 of Kolmogorov's strong law of large numbers, born on 2002-12-08, modified 2006-09-28.
Object id is 3686, canonical name is KolmogorovsStrongLawOfLargeNumbers.
Accessed 9453 times total.

Classification:
AMS MSC60F15 (Probability theory and stochastic processes :: Limit theorems :: Strong theorems)

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