Given an outcome variable $X: \Omega \to V$, and some probabilities on a sample space $\Omega $, there is a natural set of probabilities to associate with outcomes in $V$.
Suppose $X: \Omega \to V$ is a random
variable on a finite sample space $\Omega $.
Given $p: \Omega \to \R $ is a probability
distribution inducing probability measure $P:
\pow{\Omega } \to \R $, define the function $q:
V \to \R $ by
\[
q(x) = P(X = x)
\]
The function $q$ is sometimes called the distribution of $X$ (the induced distribution, induced probability distribution) of the random variable $X$.
Given a random variable $X: \Omega \to V$, and some distribution $p: \Omega \to \R $, it is common to denote the induced distribution by $p_X$. Given a distribution $q: V \to \R $ and a random variable $X: \Omega \to V$ it is common to see the notation $X \sim q$ as an abbreviation of the sentence “The random variable $X$ has distribution $q$.
Suppose $X: \Omega \to V$ is a random
variable and $f: V \to U$.
Define $Y: \Omega \to V$ by $y(\omega ) \equiv
f(x(\omega ))$ for every $\omega \in \Omega $.
We frequently denote this by $Y = f(X)$.
$Y$ is a random variable with induced
distribution $p_{Y}: \Omega \to \R $ satisfying
\[
\textstyle
p_{Y}(y) = \sum_{\omega \in \Omega \mid Y(\omega ) = y}
p(\omega ) = \sum_{x \in V \mid f(x) = b} p_X(x).
\]