# Definition:Probability Measure

## Definition

Let $\EE$ be an experiment.

### Definition 1

Let $\EE$ be defined as a measure space $\struct {\Omega, \Sigma, \Pr}$.

Then $\Pr$ is a measure on $\EE$ such that $\map \Pr \Omega = 1$.

### Definition 2

Let $\Omega$ be the sample space on $\EE$.

Let $\Sigma$ be the event space of $\mathcal E$.

A probability measure on $\EE$ is a mapping $\Pr: \Sigma \to \R$ which fulfils the Kolmogorov axioms:

 $(1)$ $:$ $\displaystyle \forall A \in \Sigma:$ $\displaystyle 0$ $\displaystyle \le$ $\displaystyle \map \Pr A \le 1$ The probability of an event occurring is a real number between $0$ and $1$ $(2)$ $:$ $\displaystyle \map \Pr \Omega$ $\displaystyle =$ $\displaystyle 1$ The probability of some elementary event occurring in the sample space is $1$ $(3)$ $:$ $\displaystyle \map \Pr {\bigcup_{i \mathop \ge 1} A_i}$ $\displaystyle =$ $\displaystyle \sum_{i \mathop \ge 1} \map \Pr {A_i}$ where $\set {A_1, A_2, \ldots}$ is a countable (possibly countably infinite) set of pairwise disjoint events That is, the probability of any one of countably many pairwise disjoint events occurring is the sum of the probabilities of the occurrence of each of the individual events

### Definition 3

Let $\Omega$ be the sample space on $\EE$.

Let $\Sigma$ be the event space of $\mathcal E$.

A probability measure on $\EE$ is a mapping $\Pr: \Sigma \to \R$ which fulfils the following axioms:

 $(\text I)$ $:$ $\displaystyle \forall A \in \Sigma:$ $\displaystyle \map \Pr A$ $\displaystyle \ge$ $\displaystyle 0$ $(\text {II})$ $:$ $\displaystyle \map \Pr \Omega$ $\displaystyle =$ $\displaystyle 1$ $(\text {III})$ $:$ $\displaystyle \forall A \in \Sigma:$ $\displaystyle \map \Pr A$ $\displaystyle =$ $\displaystyle \sum_{\bigcup \set e \mathop = A} \map \Pr {\set e}$ where $e$ denotes the elementary events of $\EE$

### Definition 4

Let $\Omega$ be the sample space on $\EE$.

Let $\Sigma$ be the event space of $\mathcal E$.

A probability measure on $\EE$ is an additive function $\Pr: \Sigma \to \R$ which fulfils the following axioms:

 $(1)$ $:$ $\displaystyle \forall A, B \in \Sigma: A \cap B = \O:$ $\displaystyle \map \Pr {A \cup B}$ $\displaystyle =$ $\displaystyle \map \Pr A + \map \Pr B$ $(2)$ $:$ $\displaystyle \map \Pr \Omega$ $\displaystyle =$ $\displaystyle 1$

## Also known as

In the context of probability theory, a probability measure is sometimes referred to as a probability function.

## Also see

• Results about probability measures can be found here.