Divergence of general random series and a special case Is there any sufficient condition in terms of moments under which 
$$ \sum_{n=1}^{\infty} X_n$$ diverges a.s.?Here $X_n$ are not independent
I am given that $\sum_n E[X_n]$ diverges. Actually, I am interested in the following special case:
Let $\{F_n\}$ be a filtration, and $\{A_n\}$ be a sequence of events such that  $A_n \in F_n$. Suppose $P(A_n) > 0 \forall n$. Then what is the weakest additional condition that will ensure the following
$$ \sum_{n=1}^{\infty} P(A_n | F_{n-1}) = \infty \mbox{  a.s.}$$
One condition is $P(A_n i.o) =1$, but I want something verifiable. 
 A: Here is one simple sufficient condition:
$\sum \mathsf{E} X_n$ diverges, and $\sum (\mathsf{Var} X_n)^{1/2} < \infty$.
Indeed, the variance condition ensures that $\sum (X_n - \mathsf{E} X_n)$ converges in $L^2$, so if $\sum \mathsf{E} X_n$ diverges, so does $\sum X_n$ (say, in probability).
A: The key observation is that even if the sum of expectations of individual $X_n$ diverges, if correlations among the $\{X_{k_1}, X_{k_2}\ldots X_{k_m}\}$ with $k_m = \sup (k_i)$ do not fall off sufficiently fast as $k_m \to \infty$, then it is possible to "save" the convergence, that is, to have a finite  probability of that the sum does not diverge.
With that concern in mind, here is 
a sufficient condition (though I would not say this is anything like a necessary condition) that $\sum_1^\infty X_n$ diverges a.s., expressed in terms of the moments (including of course the correlation expectations):
If for some $C \in \Bbb{R}, C > 0$ 
$$
\lim_{n\to \infty} n E(X_n) = C
$$
and for all partitions of integers $k = \sum_{i=1}^m k_i$ with $k > 1$, and $ k_i \geq 0$ for all $i$, 
 $$\lim_{n\to \infty} n^{k+1} \left[\prod_{i=1}^k E(X_i^{k_i})- E\left((\prod_{i=1}^k X_i^{k_i}\right)\right]=0
$$
then $\sum_{n=1}^\infty X_n$ diverges a.s.
A: Let $\mu_n$ and $\sigma_n$ be the mean and standard deviation of $X_n$.  Suppose  there is a sequence of positive numbers $k_n$ such that $\sum_n 1/k_n^2 < \infty$ while $\sum_n (\mu_n - k_n \sigma_n) = + \infty$.  By Chebyshev's inequality, $\mathbb P(X_n \ge \mu_n - k_n \sigma_n) > 1 - 1/k_n^2$, so $\sum_n 1/k_n^2 < \infty$ implies a.s. $X_n \ge \mu_n - k_n \sigma_n$ for all sufficiently large $n$, and thus a.s. $\sum_n X_n = +\infty$.
Analogous conditions could be formulated in terms of other moments.
