Does the Strong Law of Large Numbers Always Apply?

AI Thread Summary
The Strong Law of Large Numbers (SLLN) typically applies to independent and identically distributed (iid) random variables with a finite mean and bounded second moments. However, there are weaker conditions under which the SLLN still holds, such as the Kolmogorov Criterion, which allows for non-identical distributions if the series of variances converges. In cases where the distributions are identical, only the mean is necessary, even if the variance is infinite. Understanding these nuances is essential for correctly applying the SLLN in various statistical contexts. The discussion highlights the importance of recognizing these conditions to avoid misapplication of the law.
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For the Strong Law of Large Numbers, as far as I know it applies when, let say, the random variables {Xn: n=1,2,...} are iid, hence uncorrelated, their second moments have a common bound and they have a finite mean mu.
What else I must consider? Is there anyother consideretion or case when the SLLN does not apply?
 
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There are weaker conditions for the strong law to hold.

For example, the Kolmogoroff Criterion does not require the distributions be identical.
It needs sum (Vk/k2) be a convergent series (V=variance).

Alternatively if the distributions are identical, all you need is the mean - the variance may be infinite.
 
I was reading a Bachelor thesis on Peano Arithmetic (PA). PA has the following axioms (not including the induction schema): $$\begin{align} & (A1) ~~~~ \forall x \neg (x + 1 = 0) \nonumber \\ & (A2) ~~~~ \forall xy (x + 1 =y + 1 \to x = y) \nonumber \\ & (A3) ~~~~ \forall x (x + 0 = x) \nonumber \\ & (A4) ~~~~ \forall xy (x + (y +1) = (x + y ) + 1) \nonumber \\ & (A5) ~~~~ \forall x (x \cdot 0 = 0) \nonumber \\ & (A6) ~~~~ \forall xy (x \cdot (y + 1) = (x \cdot y) + x) \nonumber...
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