Multivariate normal distribution

Multivariate normal distribution and Regression
We now consider the multinormal distribution from the point of view of using X2 for predicting X1.

A multivariate normal distribution forms a Markov random field with respect to a graph G = (V, E) if the missing edges correspond to zeros on the precision matrix (the inverse covariance matrix):
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[edit] Inference ...

It is assumed that the data (for the variables) represent a sample from a multivariate normal distribution. You can examine whether or not variables are normally distributed with histograms of frequency distributions.

Thus, Linear Discriminant Analysis has assumption of Multivariate Normal distribution and all groups have the same covariance matrix.
Preferable reference for this tutorial is ...

See also: Normal distribution, Distribution, Variance, Covariance, Covariance matrix