- Stochastic vector redirects here. For the concept of a random vector, see Multivariate random variable.
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. The vectors can be either columns or rows.
Writing out the vector components of a vector as
the vector components must sum to one:
Each individual component must have a probability between zero and one:
- The mean of any probability vector is .
- The shortest probability vector has the value as each component of the vector, and has a length of .
- 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 ; where is the variance of the elements of the probability vector.