Multivariate normal random numbers

returns a matrix `R`

= mvnrnd(`mu`

,`Sigma`

,`n`

)`R`

of `n`

random vectors
chosen from the same multivariate normal distribution, with mean vector
`mu`

and covariance matrix `Sigma`

. For
more information, see Multivariate Normal Distribution.

`mvnrnd`

requires the matrix`Sigma`

to be symmetric. If`Sigma`

has only minor asymmetry, you can use`(Sigma + Sigma')/2`

instead to resolve the asymmetry.In the one-dimensional case,

`Sigma`

is the variance, not the standard deviation. For example,`mvnrnd(0,4)`

is the same as`normrnd(0,2)`

, where`4`

is the variance and`2`

is the standard deviation.

[1] Kotz, S., N. Balakrishnan, and
N. L. Johnson. *Continuous Multivariate Distributions: Volume 1: Models and
Applications.* 2nd ed. New York: John Wiley & Sons, Inc.,
2000.