Definition:Probability Mass Function/Joint

Definition
Let $X: \Omega \to \R$ and $Y: \Omega \to \R$ both be discrete random variables on $\struct {\Omega, \Sigma, \Pr}$.

Then the joint (probability) mass function of $X$ and $Y$ is the (real-valued) function $p_{X, Y}: \R^2 \to \closedint 0 1$ defined as:
 * $\forall \tuple {x, y} \in \R^2: \map {p_{X, Y} } {x, y} = \begin {cases}

\map \Pr {\set {\omega \in \Omega: \map X \omega = x \land \map Y \omega = y} } & : x \in \Omega_X \text { and } y \in \Omega_Y \\ 0 & : \text {otherwise} \end {cases}$

That is, $\map {p_{X, Y} } {x, y}$ is the probability that the discrete random variable $X$ takes the value $x$ at the same time that the discrete random variable $Y$ takes the value $y$.

Also denoted as
$\map {p_{X, Y} } {x, y}$ can also be written:
 * $\map \Pr {X = x, Y = y}$

Also see
Similarly to the individual probability mass functions of $X$ and $Y$, we have:

The latter is usually written:
 * $\ds \sum_{x \mathop \in \R} \map {p_{X, Y} } {x, y} = 1$