Pairwise uncorrelated random variables in Strong Law of Large Numbers (SLLN) On Wikipedia Strong Law of Large Numbers is given as follows. 
Let $\{X_n\}$ be a sequence of independent identically distributed $\mathbb{R}$-valued random variables with finite expectation $\mathrm{E}[X_k] = \mu, ~ \forall k \in \mathbb{N}$. Let $S_n$ denote the corresponding sequence of partial sums. Then as $n\rightarrow\infty$ we have $\frac{S_n}{n}\rightarrow \mu$ almost surely. 
My question: Is it possible to weaken the condition of $\{X_n\}$ beeing independent identically distributed (i.i.d) random variables and instead merely consider pairwise uncorrelated identically distributed $\{X_n\}$?
In 1980 Etemadi proved that i.i.d random variables can be replaced by pairwise independent random variables in the aforecited SLLN. I know that pairwise independence concept is close to pairwise uncorrelatedness. On the other hand $\mathrm{Cov}(X_i,X_j)$ by definition requires random variables $X_i$ and $X_j$ to have finite variances. Does it mean that answer to my question is "NO" because $X_i$ and $X_j$ have only finite expectations?
 A: There is likely a proof somewhere on this site but I could not find it.  Here I give a quick proof of my comment (since I originally mis-stated the result by forgetting the "lower bounded" restriction): 

Let $\{X_i\}_{i=1}^{\infty}$ be a sequence of random variables, not necessarily identically distributed and not necessarily independent, that satisfy: 
i) $E[X_i]=m_i$, where $m_i \in \mathbb{R}$ for all $i\in\{1, 2, 3, ...\}$.
ii) There is a constant $\sigma^2_{bound}$ such that $Var(X_i) \leq \sigma^2_{bound}$ for all $i \in \{1, 2, 3, ...\}$.
iii) The variables are pairwise uncorrelated, so $E[(X_i-m_i)(X_j-m_j)]=0$ for all $i \neq j$.
iv) There is a value $b \in \mathbb{R}$ such that, with prob 1, $X_i-m_i\geq b$ for all $i \in \{1, 2, 3, ...\}$. 
Define $L_n = \frac{1}{n}\sum_{i=1}^n (X_i-m_i)$.  Then $L_n\rightarrow 0$ with prob 1. 
Proof: Since the variables are pairwise uncorrelated with bounded variance, we easily find for all $n$: 
$$ E[L_n^2] = \frac{1}{n^2}\sum_{i=1}^n \sigma_i^2 \leq \frac{\sigma_{bound}^2}{n} $$
Fix $\epsilon>0$.  It follows that: 
$$ P[|L_n|>\epsilon] = P[L_n^2 \leq \epsilon^2] \leq \frac{E[L_n^2]}{\epsilon^2} \leq \frac{\sigma_{bound}^2}{n\epsilon^2} $$
Hence: 
$$ \sum_{n=1}^{\infty} P[|L_{n^2}|>\epsilon] \leq \sum_{n=1}^{\infty}\frac{\sigma_{bound}^2}{n^2\epsilon^2} < \infty $$ 
and so $L_{n^2}\rightarrow 0$ with probability 1 by the Borel-Cantelli Lemma. That is, the $L_n$ values converge over the sparse subsequence $n\in\{1, 4, 9, 16, ...\}$. 
Since $L_n \geq b$ for all $n$ and $L_{n^2}\rightarrow 0$ with probability 1, it can be shown that $L_n\rightarrow 0$ with probability 1.  $\Box$

The lower bounded condition is typically treated by writing $X_n = X_n^+ - X_n^-$ where $X_n^+$ and $X_n^-$ are nonnegative and defined $X_n^+=\max[X_n,0]$, $X_n^-=-\min[X_n,0]$.  If $X_n$ and $X_i$ are independent, then $X_n^+$ and $X_i^+$ are also independent. So the lower bounded condition can be removed for the case when variables are independent.   However, if $X_n$ and $X_i$ are uncorrelated, that does not mean $X_n^+$ and $X_i^+$ are uncorrelated.  So it is not clear to me if the lower-bounded condition can be removed when "independence" is replaced by the weaker condition "pairwise uncorrelated." 
