# Definition:Conditional Probability

## Definition

Let $\mathcal E$ be an experiment with probability space $\struct {\Omega, \Sigma, \Pr}$.

Let $A, B \in \Sigma$ be events of $\mathcal E$.

We write the **conditional probability of $A$ given $B$** as $\map \Pr {A \mid B}$, and define it as:

*the probability that $A$ has occurred, given that $B$ has occurred.*

We have that $\map \Pr {A \mid B} = \dfrac {\map \Pr {A \cap B} } {\map \Pr B}$.

This is derived as follows.

Suppose it is given that $B$ has occurred.

Then the probability of $A$ having occurred may not be $\map \Pr A$ after all.

In fact, we *can* say that $A$ has occurred if and only if $A \cap B$ has occurred.

So, if we *know* that $B$ has occurred, the conditional probability of $A$ given $B$ is $\map \Pr {A \cap B}$.

It follows then, that if we *don't* actually know whether $B$ has occurred or not, but we know its probability $\map \Pr B$, we can say that:

- The probability that $A$ and $B$ have both occurred is the conditional probability of $A$ given $B$ multiplied by the probability that $B$ has occurred.

Hence:

- $\map \Pr {A \mid B} = \dfrac {\map \Pr {A \cap B} } {\map \Pr B}$

## Sources

- 1986: Geoffrey Grimmett and Dominic Welsh:
*Probability: An Introduction*... (previous) ... (next): $\S 1.6$: Conditional probabilities: $(19)$ - 1988: Dominic Welsh:
*Codes and Cryptography*... (previous) ... (next): Notation