Why convergence in probability is defined as convergence to R.V.?

Wikipedia defines convergence in probability as

A sequence ${X_n}$ of random variables converges in probability towards the random variable $X$ if for all ε > 0 $$\lim_{n\rightarrow\infty} P(|X_n-X|>\varepsilon) = 0$$

I wonder why the limit $X$ is a random variable. I think $X$ as a r.v. can only be a constant, and defining convergence to a number $a$ instead of a r.v. $X$

$$\lim_{n\rightarrow\infty} P(|X_n-a|>\varepsilon) = 0$$

makes the definition more clear.

• @Xi'an so $X$ may not be a constant if $X_n$ and $X$ are strongly dependent? – kludg Mar 31 '18 at 12:44
• The limit $X$ is a function (defined modulo changes on a set of measure zero, as always). A "constant" in this context means a function that assigns the same value to every element in its domain. There's nothing in this definition that implies $X$ will be a constant function. – whuber Nov 5 at 14:23

That the sequence of rv's $(X_n)$ can converge to another rv $X$ is clear from the example$$X_n=X+\upsilon_n/n\qquad \upsilon_n\stackrel{\text{i.i.d.}}{\sim}\cal{N}(0,1)$$ where $X$ is an arbitrary random variable. Indeed, then$$|X_n-X|=|\upsilon_n|/n$$which converges to zero in probability:$$\mathbb{P}(|\upsilon_n|/>\epsilon)=2(1-\Phi(n\epsilon))\stackrel{n\to\infty}{\longrightarrow}0$$ Now, if the $X_n$ are independent, then they cannot converge in probability to a random variable $X$ unless $X$ is a.s. constant.