Conditional Probability Defines Probability Space
From ProofWiki
Theorem
Let $\left({\Omega, \Sigma, \Pr}\right)$ be a measure space.
Let $B \in \Sigma$ such that $\Pr \left({B}\right) > 0$.
Let $Q: \Sigma \to \R$ be the real-valued function defined as:
- $Q \left({A}\right) = \Pr \left({A \mid B}\right)$
where:
- $\Pr \left({A \mid B}\right) = \dfrac {\Pr \left({A \cap B}\right)}{\Pr \left({B}\right)}$
is the conditional probability of $A$ given $B$.
Then $\left({\Omega, \Sigma, \Pr}\right)$ is a probability space.
Proof
To prove this assertion we need to show that $Q$ is a probability measure on $\left({\Omega, \Sigma}\right)$.
As $\Pr$ is a measure, we have that:
- $\forall A \in \Omega: Q \left({A}\right) \ge 0$
Also, we have that:
| \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle Q \left({\Omega}\right)\) | \(=\) | \(\displaystyle \Pr \left({\Omega \mid B}\right)\) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | |||
| \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(=\) | \(\displaystyle \frac {\Pr \left({\Omega \cap B}\right)}{\Pr \left({B}\right)}\) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | |||
| \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(=\) | \(\displaystyle \frac {\Pr \left({B}\right)}{\Pr \left({B}\right)}\) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | Intersection with Universe | ||
| \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(=\) | \(\displaystyle 1\) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | as $\Pr \left({B}\right) > 0$ |
Now, suppose that $A_1, A_2, \ldots$ are disjoint events in $\Sigma$.
Then:
| \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle Q \left({\bigcup_i A_i}\right)\) | \(=\) | \(\displaystyle \frac 1 {\Pr \left({B}\right)} \Pr \left({\left({\bigcup_i A_i}\right) \cap B}\right)\) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | |||
| \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(=\) | \(\displaystyle \frac 1 {\Pr \left({B}\right)} \Pr \left({\bigcup_i A_i}\right) \left({A_i \cap B}\right)\) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | Intersection Distributes over Union | ||
| \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(=\) | \(\displaystyle \frac {\Pr \left({B}\right)}{\sum \Pr \left({A_i \cap B}\right)}\) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | as $\Pr$ satisfies the Kolmogorov axioms | ||
| \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | \(=\) | \(\displaystyle \sum Q \left({A_i}\right)\) | \(\displaystyle \) | \(\displaystyle \) | \(\displaystyle \) | by definition. |
$\blacksquare$
Sources
- Geoffrey Grimmett and Dominic Welsh: Probability: An Introduction (1986): $\S 1.6$: Theorem $1 \text {A}$