Existence of Probability Space and Discrete Random Variable
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Theorem
Let $I$ be an arbitrary countable indexing set.
Let $S = \set {s_i: i \in I} \subset \R$ be a countable set of real numbers.
Let $\set {\pi_i: i \in I} \subset \R$ be a countable set of real numbers which satisfies:
- $\ds \forall i \in I: \pi_i \ge 0, \sum_{i \mathop \in I} \pi_i = 1$
Then there exists:
- a probability space $\struct {\Omega, \Sigma, \Pr}$
and:
- a discrete random variable $X$ on $\struct {\Omega, \Sigma, \Pr}$
such that the probability mass function $p_X$ of $X$ is given by:
\(\ds \map {p_X} {s_i}\) | \(=\) | \(\ds \pi_i\) | if $i \in I$ | |||||||||||
\(\ds \map {p_X} s\) | \(=\) | \(\ds 0\) | if $s \notin S$ |
Proof
Take $\Omega = S$ and $\Sigma = \powerset S$ (the power set of $S$).
Then let:
- $\ds \map \Pr A = \sum_{i: s_i \mathop \in A} \pi_i$
for all $A \in \Sigma$.
Then we can define $X: \Omega \to \R$ by:
- $\forall \omega \in \Omega: \map X \omega = \omega$
This suits the conditions of the assertion well enough.
$\blacksquare$
Motivation
What this theorem allows us to do is ignore all the detail of sample spaces, event spaces and probability measure, and merely say:
- For each $i \in I$, let $X$ be a (discrete) random variable which takes value $s_i$ with probability $\pi_i$
and we know that such a random variable exists without having construct it every time.
Sources
- Geoffrey Grimmett and Dominic Welsh: Probability: An Introduction ... (previous) ... (next): $\S 2.1$: Probability mass functions: Theorem $2 \text{A}$