# Passing a limit into expectation

Given a random process $\{X_n\}_{n\in \mathbb{Z}^+}$ with a real state space (i.e., $X_n$ takes on real numbers), what can you say about these two expressions? \begin{align*} \lim_{n\rightarrow\infty} \mathbb{E}\left[X_n\mathop{\big|}X_0=j\right] & \quad (1) \\ \mathbb{E}\left[\lim_{n\rightarrow\infty} X_n\mathop{\big|} X_0 = j\right] & \quad(2) \end{align*}

(or also considering omitting the conditional statement on $X_0$... I'm not sure whether it matters or not).

I did some Googling, and I found something called Fatou's lemma, but I have no background in measure theory, and most of the Wikipedia article is way over my head.

My questions

• When can you pass the limit in and out (equality), and when is it an inequality (Fatou's lemma)?
• Does anything change if $\{X_n\}$ is a Markov chain?
• What if it was a continuous process $\{X_t\}_{t\in\mathbb{R}^+}$ instead?
• How do you interpret (explain in words) what those two expressions mean and what the difference is between them?
• Is there any causal relationship between existence of one limit and the other? For example, does the existence of the limit in $(1)$ imply that the limits in $(2)$ all exist?

I'm sorry if my questions don't make much sense or if I am using terminology incorrectly; I am not very familiar with this material, and would like to learn more about it. Any helpful information would be much appreciated.

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Try Lebesgue dominated convergence theorem: if $X_n\to X$ almost surely with respect to the probability measure $\mathbb P_j(\ )=\mathbb P(\ \mid X_0=j)$, and if $|X_n|\leqslant Y$ almost surely and for every $n$, with $\mathbb E_j(Y)$ finite, then $\lim\limits_{n\to\infty}\mathbb E_j(X_n)=\mathbb E_j(X)$, that is, $$\lim\limits_{n\to\infty}\mathbb E(X_n\mid X_0=j)=\mathbb E\left(\lim\limits_{n\to\infty}X_n\,{\Large\mid}\, X_0=j\right).$$

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