multivariate distribution

The multivariate distribution is used when there are more than two colors.

Sets of central moments can be defined for both univariate and multivariate distributions.

They allow the joining of multiple univariate distributions to a single multivariate distribution.

It is also possible to modify some attributes of a multivariate distribution using an appropriately constructed transformation.

Another possibility is to present survey results by means of statistical models in the form of a multivariate distribution mixture.

See the formula below under multivariate distribution.

Multivariate distribution and correlation in the late 19th and 20th century.

This is in contrast to a multivariate distribution, the probability distribution of a random vector.

The multivariate normal distribution is a commonly encountered multivariate distribution.

Inversion formulas for multivariate distributions are available.

This concept extends to multivariate distributions.

Conversely, every positive semi-definite matrix is the covariance matrix of some multivariate distribution.

The Hodges-Lehmann estimator has been generalized to multivariate distributions.

These multivariate distributions are:

The strength of the procedure is its ability to not only incorporate sampling error, but also the factorial structure and multivariate distribution of the items.

In multivariate distributions, the classic Metropolis-Hastings algorithm as described above involves choosing a new multi-dimensional sample point.

For multivariate distributions, formulae similar to those above apply with the symbols X and/or Y being interpreted as vectors.

He has also published in diverse areas such as ranking and selection, multivariate distributions, characterization, and mixtures of distributions.

Here the underlying random variables may be random vectors, each having the same dimension, in which case the mixture distribution is a multivariate distribution.

A more sophisticated mathematical technique for performing such a reconstruction is the method of maximum likelihood based on a multivariate distribution of the neuronal responses.

This measure is also known as the joint probability distribution, the joint distribution, or the multivariate distribution of the random vector.

In statistical theory, one long-established approach to higher-order statistics, for univariate and multivariate distributions is through the use of cumulants and joint cumulants.

Estimation of covariance matrices then deals with the question of how to approximate the actual covariance matrix on the basis of a sample from the multivariate distribution.

With Aurelien Alfonsi (2005), Brigo introduced new families of multivariate distributions in statistics through the periodic copula function concept.

In the case of only two random variables, this is called a bivariate distribution, but the concept generalizes to any number of random variables, giving a multivariate distribution.