# Gaussian distribution is isotropic?

I was in a seminar today and the lecturer said that the gaussian distribution is isotropic. What does it mean for a distribution to be isotropic? It seems like he is using this property for the pseudo-independence of vectors where each entry is sampled from the normal distribution.

• In general, a multivariate normal distribution can be anisotropic depending on the covariance matrix. There has. clearly been some miscommunication somewhere along the way. – Brian Borchers Oct 30 '16 at 18:52

TLDR: An isotropic gaussian is one where the covariance matrix is represented by the simplified matrix $\Sigma = \sigma^{2}I$.

Some motivations:

$$\mathcal{N}(\mu,\,\Sigma)$$

where $\mu$ is the mean and $\Sigma$ is the covariance matrix.

Consider how the number of free parameters in this Gaussian grows as the number of dimensions grows.

$\mu$ will have a linear growth. $\Sigma$ will have a quadratic growth!

This quadratic growth can be very computationally expensive, so $\Sigma$ is often restricted as $\Sigma = \sigma^{2}I$ where $\sigma^{2}I$ is a scalar variance multiplied by an identity matrix.

Note that this results in $\Sigma$ where all dimensions are independent and where the variance of each dimension is the same. So the gaussian will be circular/spherical.

Disclaimer: Not a mathematician, and I only just learned about this so may be missing some things :)

Hope that helps!

• The variables of the multivariate Gaussian may not be independent, even if they have zero covariance. Covariance is a measure of only linear association. Independence implies zero covariance but not vice versa. See this example. – mloning Nov 29 '19 at 9:10

I'd just like to add a bit of visuals to the other answers.

When the variables are independent, i.e. the distrubtion is isotropic, it means that the distribution is aligned with the axis.

For example, for $$\Sigma = \begin{pmatrix}1 & 0 \\ 0 & 30\end{pmatrix}$$, you'd get something like this: So, what happens when it is not isotropic? For example, when $$\Sigma = \begin{pmatrix}1 & 15 \\ 15 & 30\end{pmatrix}$$, the distribution appears "rotated", no longer aligned with the axes: Note that this is just an example, the $$\Sigma$$ above is invalid since it is not PSD.

Code:

import numpy as np
from matplotlib import pyplot as plt

pts = np.random.multivariate_normal([0, 0], [[1,15],[15,31]], size=10000, check_valid='warn')

plt.scatter(pts[:, 0], pts[:, 1], s=1)
plt.xlim((-30,30))
plt.ylim((-30,30))


I am not a math major student but I will give a try to describe my understanding: an isotropic gaussian distribution means a multidimensional gaussian distribution with its variance matrix as an identity matrix multiplied by the same number on its diagonal. Each dimension can be seen as an independent one-dimension gaussian distribution (no covariance exists).