2.3 Probability axioms

Probability of an event


A number that encodes our knowledge or belief about the collective “likelihood” of the elements of the event.


\(\text{P}(A)\): the probability of event \(A\).

Probability

Roll a fair die 🎲. Which of the two would you bet on?

  • It lands on an even number
  • It lands on an odd number

Roll two dice 🎲 🎲. Which of the two would you bet on?

  • Total of 7
  • Total of 12

Probability axioms

  1. Non-negativity \[\text{P}(A)\geq0,\; \text{for any event $A$}\]
  2. Normalization \[ \text{P}(\Omega)=1\]
  3. Additivity \[ \text{If } A \cap B = \Phi, \text{ then } \text{P}(A \cup B) = \text{P}(A) + \text{P}(B)\]

A more general form of the additivity


If \(A_1, A_2, A_3, \cdots\) is an infinite collection of disjoint events, then

\[ \text{P}(A_1 \cup A_2 \cup A_3 \cup \cdots) = \text{P}(A_1) + \text{P}(A_2) + \text{P}(A_3) + \cdots\]

Discussion

Recall the non-negativity axiom:

\[\text{P}(A)\geq0,\; \text{for any event $A$}\]


Do we need to also add the following as an axiom?

\[\text{P}(A) \leq 1, \;\text{for any event } A\]

Proposition

\[\text{P}(A) + \text{P}(A^c) = 1, \;\text{for any event } A\]

Proposition

\[ \text{P}(A \cup B) = \text{P}(A) + \text{P}(B) - \text{P}(A \cap B), \]

\[\text{for any events $A$ and $B$}\]

\(A\) \(B\) \(\Omega\) \(A\) \(B\) \(\Omega\) \(A\) \(B\) \(\Omega\)

Exercise

Assumptions

  • 75% of the students use Instagram
  • 35% use LinkedIn
  • 25% use both

We randomly select a student. What is the probability that