Skip to content

Mathematics · Ch 11 — Probability Distributions

Probability Mass Function

11.3.2

Probability Mass Function

Definition 11.3 (Probability mass function). If XX is a discrete random variable taking the values x1,x2,x3,…,xn,…x_1,x_2,x_3,\dots,x_n,\dots, the function f(⋅)f(\cdot) (also written p(⋅)p(\cdot)) defined by

f(xk)=P(X=xk),k=1,2,3,…f(x_k)=P(X=x_k),\qquad k=1,2,3,\dots

is the probability mass function (pmf) of XX.

Theorem 11.1 (characterisation, without proof). f(x)f(x) is a pmf if and only if

(i) f(xk)≥0f(x_k)\ge0 for every kk, and (ii) ∑kf(xk)=1\displaystyle\sum_k f(x_k)=1.

Note

The set of values {f(xk)=P(X=xk)}\{f(x_k)=P(X=x_k)\} is also called the probability distribution of the discrete random variable. It can be presented in three equivalent ways: (a) as a table, (b) as a graph, or (c) as an algebraic expression.

Worked illustrations. …