In probability theory and statistics, the multivariate normal distribution or multivariate Gaussian distribution, is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. One possible definition is that a random vector is said to be k-variate normally distributed if every linear combination of its k components has a univariate normal distribution. However, its importance derives mainly from the multivariate central limit theorem. The multivariate normal distribution is often used to describe, at least approximately, any set of (possibly) correlated real-valued random variables each of which clusters around a mean value.
Read more about Multivariate Normal Distribution: Notation and Parametrization, Definition, Conditional Distributions, Marginal Distributions, Affine Transformation, Geometric Interpretation, Estimation of Parameters, Bayesian Inference, Multivariate Normality Tests, Drawing Values From The Distribution
Famous quotes containing the words normal and/or distribution:
“The normal present connects the past and the future through limitation. Contiguity results, crystallization by means of solidification. There also exists, however, a spiritual present that identifies past and future through dissolution, and this mixture is the element, the atmosphere of the poet.”
—Novalis [Friedrich Von Hardenberg] (1772–1801)
“There is the illusion of time, which is very deep; who has disposed of it? Mor come to the conviction that what seems the succession of thought is only the distribution of wholes into causal series.”
—Ralph Waldo Emerson (1803–1882)