Series of independent random variables are independent again Let $\{X^i_j : i=1,..n, j\in\mathbb{N}\}$ be an independent set of random variables on a probability space $(\Omega, A, \mathbb{P})$,
$$X^k_l: \Omega \to \mathbb{R^+} := \{x \in \mathbb{R} : x \ge 0\}$$
$$Y^m := \sum_{i=1}^\infty X^m_i$$
Show that
$$Y^1, ..., Y^n$$
is an independent set of random variables
Defintion of independence for a set of random variables:
A set of random variables is mutually independent if and only if for any finite subset $X_1, \ldots, X_n$ and any finite sequence of numbers $a_1, \ldots, a_n$, the events $\{X_1 \le a_1\}, \ldots, \{X_n \le a_n\}$ are mutually independent events.
Edit: The random variables are now non-negative
 A: For each $1 \leq i \leq n$, define $X^i:=(X^i_j)_{j\in\mathbb{N}}$. More explicitly, $X^i$ is the mapping from $\Omega \to \mathbb{R}^{\mathbb{N}}$ defined by $\omega \mapsto (j \mapsto X^i_j(\omega))$. It is measurable with respect to the $\sigma$-algebra $\mathcal{F}$ on $\mathbb{R}^{\mathbb{N}}$ generated by the canonical projections $\pi_j:\mathbb{R}^{\mathbb{N}}\to \mathbb{R}$.
I claim that $X^1,...,X^n$ are independent. To show this, we'd need to show that $P(X^1 \in A^1,...,X^n\in A^n)=P(X^1\in A^1)\cdots P(X^n \in A^n)$ for all $A^i \subset \mathbb{R}^{\mathbb{N}}$ with $A^i \in \mathcal{F}$. It suffices to prove this when each of the $A^i$ has the form $\bigcap_{j=1}^{n_i} \pi_j^{-1}(B^i_j)$, where the $B^i_j \subset \mathbb{R}$ are Borel sets and $n_i \in \mathbb{N}$ (because such sets form a $\pi$-system generating $\mathcal{F}$). But this follows directly from the independence of the entire collection $\{ X^i_j \}_{i,j}$.
Define $f:= \sup_n \sum_{j=1}^n \pi_j: \mathbb{R}^{\mathbb{N}}\to \mathbb{R}$. Then $f(X^i)=Y^i$ for each $1 \leq i \leq n$ (because the $X^i$ are non-negative), so since the $X^i$ are independent, it follows that the $Y^i$ are independent.
