r = mvnrnd (mu, sigma) returns an N-by-D matrix r of random vectors chosen from the multivariate normal distribution with mean vector mu and covariance matrix sigma. mu is an N-by-D matrix, and mvnrnd generates each N of r using the corresponding N of mu. sigma is a D-by-D symmetric positive semi-definite matrix, or a D-by-D-by-N array. If sigma is an array, mvnrnd generates each N of r using the corresponding page of sigma, i.e., mvnrnd computes r(i,:) using mu(i,:) and sigma(:,:,i). If the covariance matrix is diagonal, containing variances along the diagonal and zero covariances off the diagonal, sigma may also be specified as a 1-by-D matrix or a 1-by-D-by-N array, containing just the diagonal. If mu is a 1-by-D vector, mvnrnd replicates it to match the trailing dimension of SIGMA.

r = mvnrnd (mu, sigma, n) returns a N-by-D matrix R of random vectors chosen from the multivariate normal distribution with 1-by-D mean vector mu, and D-by-D covariance matrix sigma.

r = mvnrnd (mu, sigma, n, T) supplies the Cholesky factor T of sigma, so that sigma(:,:,J) == T(:,:,J)’*T(:,:,J) if sigma is a 3D array or sigma == T’*T if sigma is a matrix. No error checking is done on T.

[r, T] = mvnrnd (…) returns the Cholesky factor T, so it can be re-used to make later calls more efficient, although there are greater efficiency gains when SIGMA can be specified as a diagonal instead.

See also: mvncdf, mvnpdf

Source Code: mvnrnd