# Mathematica: Faster Multinormal Sampling with Different Covariance Matrices

Let's say we are given a set of different covariance matrices. We wish to draw a single sample of the multivariate normal distribution from each of these covariance matrices.

Assuming the mean vector is zero, we would code this
for a single draw from a single covariance matrix
`sigma` in Mathematica:

MultinormalDraw[sigma_] := RandomVariate[MultinormalDistribution[sigma]]

To demonstrate why this is slow, we will use a hundred randomly generated $200\times 200$ covariance matrices (that is, matrices that are both symmetric and positive definite):