Concept

# Multivariate normal distribution

Summary
In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. One 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. 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. Definitions Notation and parameterization The multivariate normal distribution of a k-dimensional random vector \mathbf{X} = (X_1,\ldots,X_k)^{\mathrm T} can be written in the following notation: : \mathbf{X}\ \sim\ \mathcal{N}(\boldsymbol\mu,, \boldsymbol\Sigma),
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