Below is a copy of an answer I gave here https://stats.stackexchange.com/questions/2932/metric-spaces-and-the-support-of-a-random-variable/20769#20769

Here are some technical conveniences of separable metric spaces

(a) If $X$ and $X'$ take values in a separable metric space $(E,d)$ then the event $\{X=X'\}$ is measurable, and this allows to define random variables in the elegant way: a random variable is the equivalence class of $X$ for the "almost surely equals" relation (note that the normed vector space $L^p$ is a set of equivalence class)

(b) The distance $d(X,X')$ between the two $E$-valued r.v. $X, X'$ is measurable; in passing this allows to define the space $L^0$ of random variables equipped with the topology of convergence in probability

(c) Simple r.v. (those taking only finitely many values) are dense in $L^0$

And some techical conveniences of complete separable (Polish) metric spaces :

(d) Existence of the conditional law of a Polish-valued r.v.

(e) Given a morphism between probability spaces, a Polish-valued r.v. on the first probability space always has a copy in the second one

Below is a copy of an answer I gave here https://stats.stackexchange.com/questions/2932/metric-spaces-and-the-support-of-a-random-variable/20769#20769

Here are some technical conveniences of separable metric spaces

(a) If $X$ and $X'$ take values in a separable metric space $(E,d)$ then the event $\{X=X'\}$ is measurable, and this allows to define random variables in the elegant way: a random variable is the equivalence class of $X$ for the "almost surely equals" relation (note that the normed vector space $L^p$ is a set of equivalence class)

(b) The distance $d(X,X')$ between the two $E$-valued r.v. $X, X'$ is measurable; in passing this allows to define the space $L^0$ of random variables equipped with the topology of convergence in probability

(c) Simple r.v. (those taking only finitely many values) are dense in $L^0$

And some techical conveniences of complete separable (Polish) metric spaces :

(d) Existence of the conditional law of a Polish-valued r.v.

(e) Given a morphism between probability spaces, a Polish-valued r.v. on the first probability space always has a copy in the second one

(f) Doob-Dynkin functional representation: if $Y$ is a Polish-valued r.v. measurable w.r.t. the $\sigma$-field $\sigma(X)$ generated by a random element $X$ in any measurable space, then $Y = h(X)$ for some measurable function $h$.

Below is a copy of an answer I gave here http://stats.stackexchange.com/questions/2932/metric-spaces-and-the-support-of-a-random-variable/20769#20769

Here are some technical conveniences of separable metric spaces

(a) If $X$ and $X'$ take values in a separable metric space $(E,d)$ then the event $\{X=X'\}$ is measurable, and this allows to define random variables in the elegant way: a random variable is the equivalence class of $X$ for the "almost surely equals" relation (note that the normed vector space $L^p$ is a set of equivalence class)

(b) The distance $d(X,X')$ between the two $E$-valued r.v. $X, X'$ is measurable; in passing this allows to define the space $L^0$ of random variables equipped with the topology of convergence in probability

(c) Simple r.v. (those taking only finitely many values) are dense in $L^0$

And some techical conveniences of complete separable (Polish) metric spaces :

(d) Existence of the conditional law of a Polish-valued r.v.

(e) Given a morphism between probability spaces, a Polish-valued r.v. on the first probability space always has a copy in the second one

Below is a copy of an answer I gave here https://stats.stackexchange.com/questions/2932/metric-spaces-and-the-support-of-a-random-variable/20769#20769

Here are some technical conveniences of separable metric spaces

(a) If $X$ and $X'$ take values in a separable metric space $(E,d)$ then the event $\{X=X'\}$ is measurable, and this allows to define random variables in the elegant way: a random variable is the equivalence class of $X$ for the "almost surely equals" relation (note that the normed vector space $L^p$ is a set of equivalence class)

(b) The distance $d(X,X')$ between the two $E$-valued r.v. $X, X'$ is measurable; in passing this allows to define the space $L^0$ of random variables equipped with the topology of convergence in probability

(c) Simple r.v. (those taking only finitely many values) are dense in $L^0$

And some techical conveniences of complete separable (Polish) metric spaces :

(d) Existence of the conditional law of a Polish-valued r.v.

(e) Given a morphism between probability spaces, a Polish-valued r.v. on the first probability space always has a copy in the second one