Distribution of sum of possibly non-independent Bernoulli random variables with known variance-covariance matrix I wonder if there are any results concerning the distribution of sums of possibly non-IID Bernoulli random variables when covariances in all pairs of r.v.'s are known.
To make this more concrete consider the following problem. Let $X_i$ for $i = 1, \ldots, k$ be a sequence of non-IID Bernoulli random variables with known parameters $p_i$ and $X$ the sum over all $X_i$'s:
$$
X = \sum_{i=1}^k X_i \quad\text{where}\quad X_i \sim \mathcal{B}(p_i)
$$
Moreover, the full set of covariances in all pairs $X_i, X_j$ is known.
What is the distribution of $X$?
I know that in the case when all $X_i$ are independent the solution is Poisson-Binomial distribution but I am interested particularily in the case when at least in some pairs $X_i, X_j$ covariances are non-zero.
[EDIT]
What if I could derive bivariate distributions for all pairs $X_i$ and $X_j$? Would this allow for deriving also the exact (or at least approximate) distribution of the sum $Z = \sum_i X_i$?

Any distribution on the integers $0,1,\dots,k$ can be written as a sum of $k$ non-IID Bernoullis.
Let $Z$ be a variable with the target distribution. Now,

*

*$p(X_1)= p(Z>0)$

*if $X_1=1$, $p(X_2)= p(Z>1|Z\geq 1)$ else $p(X_2)=0$

*if $X_2=1$, $p(X_3)=p(Z>2|Z\geq 2)$ else $p(X_3)=0$

*if $X_3=1$, $p(X_4)=p(Z>3|Z\geq 3)$ else $p(X_4)=0$

*and so on

(Edit:) so $Z=\sum_i Xi$
