# Probability Vector

Stochastic vector redirects here. For the concept of a random vector, see Multivariate random variable.

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.

Here are some examples of probability vectors: $x_0=\begin{bmatrix}0.5 \\ 0.25 \\ 0.25 \end{bmatrix},\; x_1=\begin{bmatrix} 0 \\ 1 \\ 0 \end{bmatrix},\; x_2=\begin{bmatrix} 0.65 \\ 0.35 \end{bmatrix},\; x_3=\begin{bmatrix}0.3 \\ 0.5 \\ 0.07 \\ 0.1 \\ 0.03 \end{bmatrix}.$

Writing out the vector components of a vector as

the vector components must sum to one:

One also has the requirement that each individual component must have a probability between zero and one:

for all . These two requirements show that stochastic vectors have a geometric interpretation: A stochastic vector is a point on the "far face" of a standard orthogonal simplex. That is, a stochastic vector uniquely identifies a point on the face opposite of the orthogonal corner of the standard simplex.