ThmDex – An index of mathematical definitions, results, and conjectures.
Sum rule for simple marginal probability
Formulation 0
Let $X \in \text{Random} \{ x_1, \ldots, x_N \}$ and $Y \in \text{Random} \{ y_1, \ldots, y_M \}$ each be a D5723: Simple random variable.
Let $n \in 1, \ldots, N$ be a D5094: Positive integer.
Then \begin{equation} \mathbb{P}(X = x_n) = \sum_{m = 1}^M \mathbb{P}(X = x_n, Y = y_m) \end{equation}
Proofs
Proof 0
Let $X \in \text{Random} \{ x_1, \ldots, x_N \}$ and $Y \in \text{Random} \{ y_1, \ldots, y_M \}$ each be a D5723: Simple random variable.
Let $n \in 1, \ldots, N$ be a D5094: Positive integer.
Applying results
(i) R4331: Probability of binary intersection with an almost sure event
(ii) R4840: Intersection distributes over finite union
(iii) R4841: Finite disjoint additivity of probability measure

we have \begin{equation} \begin{split} \mathbb{P}(X = x_n) & = \mathbb{P} \left( \{ X = x_n \} \cap \bigcup_{m = 1}^M \{ Y = y_m \} \right) \\ & = \mathbb{P} \left( \{ X = x_n \} \cap \bigcup_{m = 1}^M \{ Y = y_m \} \right) \\ & = \mathbb{P} \left( \bigcup_{m = 1}^M (\{ X = x_n \} \cap \{ Y = y_m \}) \right) \\ & = \mathbb{P} \left( \bigcup_{m = 1}^M (\{ X = x_n, Y = y_m \}) \right) \\ & = \sum_{m = 1}^M \mathbb{P}(X = x_n, Y = y_m) \end{split} \end{equation} $\square$