Normally each element of a random vector is a real number.

Those random vectors have components in the three directions of space.

A similar result holds for the joint distribution of a random vector.

In the following, the entries of the random vector are assumed to be independent.

An interesting fact derived in order to prove this result, is that the random vectors and are independent.

Let be the support of the random vector .

More explicitly, let be a uniform random vector with and .

Pattern features as components in a random vector representation.

One is interested in the expectation of a response function applied to some random vector .

A family of random vectors in is called a Lévy basis on if:

Define the random vector Y as composed of the elements:

A specific realization of this random vector will be denoted by .

Plot your translation, Jackie, and execute a random vector change the minute we cross the wall.

Since this random vector can lie anywhere in n-dimensional space, it has n degrees of freedom.

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

High-dimensional statistics relies on the theory of random vectors.

Mutation amounts to adding a random vector, a perturbation with zero mean.

Note: to amplify the probability of success, one can repeat the procedure with different random vectors and take the majority answer.

The Euclidean norm of a bivariate normally distributed random vector.

This definition of expectation as inner product can be extended to random vectors as well.

In statistics, a random vector x is classically represented by a probability density function.

This can be represented as an n-dimensional random vector:

Suppose that we have a sample of realizations of the random vector .

Due to the randomness of , is a uniformly random vector from .

Let X be a d-dimensional random vector expressed as column vector.