Definition:Marginal Probability Mass Function
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Definition
Let $\left({\Omega, \Sigma, \Pr}\right)$ be a probability space.
Let $X: \Pr \to \R$ and $Y: \Pr \to \R$ both be discrete random variables on $\left({\Omega, \Sigma, \Pr}\right)$.
Let $p_{X, Y}$ be the joint probability mass function of $X$ and $Y$.
Then the probability mass functions $p_X$ and $p_Y$ are called the marginal (probability) mass functions of $X$ and $Y$ respectively.
The marginal mass function can be obtained from the joint mass function:
\(\ds p_X \left({x}\right)\) | \(=\) | \(\ds \Pr \left({X = x}\right)\) | ||||||||||||
\(\ds \) | \(=\) | \(\ds \sum_{y \mathop \in \operatorname{Im} \left({Y}\right)} \Pr \left({X = x, Y = y}\right)\) | ||||||||||||
\(\ds \) | \(=\) | \(\ds \sum_y p_{X, Y} \left({x, y}\right)\) |
Sources
- 1986: Geoffrey Grimmett and Dominic Welsh: Probability: An Introduction ... (previous) ... (next): $\S 3.1$: Bivariate discrete distributions: $(4)$