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In mathematics and statistics, a probability vector or stochastic vector is a vector with non-negative entries that add up to one.

The positions (indices) of a probability vector represent the possible outcomes of a discrete random variable, and the vector gives us the probability mass function of that random variable, which is the standard way of characterizing a discrete probability distribution.^[1]

Examples

Here are some examples of probability vectors. The vectors can be either columns or rows.

$x_{0}={\begin{bmatrix}0.5\\0.25\\0.25\end{bmatrix}},$
$x_{1}={\begin{bmatrix}0\\1\\0\end{bmatrix}},$
$x_{2}={\begin{bmatrix}0.65&0.35\end{bmatrix}},$
$x_{3}={\begin{bmatrix}0.3&0.5&0.07&0.1&0.03\end{bmatrix}}.$

Geometric interpretation

Writing out the vector components of a vector $p$ as

p={\begin{bmatrix}p_{1}\\p_{2}\\\vdots \\p_{n}\end{bmatrix}}\quad {\text{or}}\quad p={\begin{bmatrix}p_{1}&p_{2}&\cdots &p_{n}\end{bmatrix}}

the vector components must sum to one:

\sum _{i=1}^{n}p_{i}=1

Each individual component must have a probability between zero and one:

0\leq p_{i}\leq 1

for all $i$ . Therefore, the set of stochastic vectors coincides with the standard $(n-1)$ -simplex. It is a point if $n=1$ , a segment if $n=2$ , a (filled) triangle if $n=3$ , a (filled) tetrahedron $n=4$ , etc.

Properties

The mean of any probability vector is $1/n$ .
The shortest probability vector has the value $1/n$ as each component of the vector, and has a length of ${\textstyle 1/{\sqrt {n}}}$ .
The longest probability vector has the value 1 in a single component and 0 in all others, and has a length of 1.
The shortest vector corresponds to maximum uncertainty, the longest to maximum certainty.
The length of a probability vector is equal to ${\textstyle {\sqrt {n\sigma ^{2}+1/n}}}$ ; where $\sigma ^{2}$ is the variance of the elements of the probability vector.