The multivariate Gaussian distribution of an $n$-dimensional vector $\boldsymbol{x}=(x_1, x_2, \cdots, x_n)$ may be written

$$ p(\boldsymbol{x}; \boldsymbol{\mu}, \boldsymbol{\Sigma}) = \frac{1}{\sqrt{(2\pi)^n|\boldsymbol{\Sigma}|}} \exp\left( -\frac{1}{2}(\boldsymbol{x}-\boldsymbol{\mu})^\mathrm{T}\boldsymbol{\Sigma}^{-1}(\boldsymbol{x}-\boldsymbol{\mu})\right), $$

where $\boldsymbol{\mu}$ is the $n$-dimensional mean vector and $\boldsymbol{\Sigma}$ is the $n \times n$ covariance matrix.

To visualize the magnitude of $p(\boldsymbol{x}; \boldsymbol{\mu}, \boldsymbol{\Sigma})$ as a function of all the $n$ dimensions requires a plot in $n+1$ dimensions, so visualizing this distribution for $n>2$ is tricky. The code below calculates and visualizes the case of $n=2$, the *bivariate* Gaussian distribution.

The plot uses the colormap `viridis`

, which was introduced in Matplotlib v.1.4 – you can replace it with any other sane colormap, such as `hot`

if you're on an earlier version of Matplotlib.

```
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
# Our 2-dimensional distribution will be over variables X and Y
N = 60
X = np.linspace(-3, 3, N)
Y = np.linspace(-3, 4, N)
X, Y = np.meshgrid(X, Y)
# Mean vector and covariance matrix
mu = np.array([0., 1.])
Sigma = np.array([[ 1. , -0.5], [-0.5, 1.5]])
# Pack X and Y into a single 3-dimensional array
pos = np.empty(X.shape + (2,))
pos[:, :, 0] = X
pos[:, :, 1] = Y
def multivariate_gaussian(pos, mu, Sigma):
"""Return the multivariate Gaussian distribution on array pos.
pos is an array constructed by packing the meshed arrays of variables
x_1, x_2, x_3, ..., x_k into its _last_ dimension.
"""
n = mu.shape[0]
Sigma_det = np.linalg.det(Sigma)
Sigma_inv = np.linalg.inv(Sigma)
N = np.sqrt((2*np.pi)**n * Sigma_det)
# This einsum call calculates (x-mu)T.Sigma-1.(x-mu) in a vectorized
# way across all the input variables.
fac = np.einsum('...k,kl,...l->...', pos-mu, Sigma_inv, pos-mu)
return np.exp(-fac / 2) / N
# The distribution on the variables X, Y packed into pos.
Z = multivariate_gaussian(pos, mu, Sigma)
# Create a surface plot and projected filled contour plot under it.
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, Z, rstride=3, cstride=3, linewidth=1, antialiased=True,
cmap=cm.viridis)
cset = ax.contourf(X, Y, Z, zdir='z', offset=-0.15, cmap=cm.viridis)
# Adjust the limits, ticks and view angle
ax.set_zlim(-0.15,0.2)
ax.set_zticks(np.linspace(0,0.2,5))
ax.view_init(27, -21)
plt.show()
```

The matrix multiplication in the exponential is achieved with NumPy's einsum method. The single call:

```
fac = np.einsum('...k,kl,...l->...', pos-mu, Sigma_inv, pos-mu)
```

could be written, in the two-dimensional case as:

```
fac = np.einsum('ijk,kl,ijl->ij', x-mu, Sigma_inv, x-mu)
```

and is equivalent to the following sequence of products:

```
fac = np.einsum('ijk,kl->ijl', pos-mu, Sigma_inv)
fac = np.einsum('ijk,ijk->ij', fac, pos-mu)
```

The first call here is simply the dot product of the $n_x \times n_y \times 2$ array `pos-mu`

and the $2 \times 2$ covariance matrix `Sigma_inv`

(it could be replaced with `np.dot(pos-mu, Sigma_inv)`

) and results in another $n_x \times n_y \times 2$ array. This is really a $n_x \times n_y$ matrix of vectors, and NumPy has a hard time taking the appropriate dot product with another $n_x \times n_y$ matrix of vectors, `pos-mu`

: we want a matrix of vector dot products, not a vector of matrix dot products. `einsum`

allows us to explicitly state which indexes are to be summed over (the last dimension, indicated by the repeated `k`

in the string of subscripts `'ijk,ijk->ij'`

).

*Note*: Since SciPy 0.14, there has been a `multivariate_normal`

function in the `scipy.stats`

subpackage which can also be used to obtain the multivariate Gaussian probability distribution function:

```
from scipy.stats import multivariate_normal
F = multivariate_normal(mu, Sigma)
Z = F.pdf(pos)
```

## Comments

Comments are pre-moderated. Please be patient and your comment will appear soon.

## Santhos kumar 1 year, 11 months ago

can we plot two normal surfaces in same graph

Link | Reply## christian 1 year, 11 months ago

Do you mean the sum of two normal surfaces? Sure – just define Z = multivariate_gaussian(pos1, mu1, Sigma1) + multivariate_gaussian(pos2, mu2, Sigma2)

Link | ReplyFor a stack of surfaces, you'd need to alter the code a bit.

## Boimans 1 year, 8 months ago

Great explanation. But I would love to take it a step further. How would I plot a conditional distribution (say one of the x vars = -1) on the same 3D plot? Like this: https://www.researchgate.net/profile/Matthew_Piggott2/publication/318429539/figure/fig1/AS:614381096284160@1523491276935/Linking-two-points-using-a-bivariate-Gaussian-distribution-enables-the-conditional-pdf_W640.jpg

Link | Reply## christian 1 year, 8 months ago

If you only want something illustrative, you could get the index of the X coordinate value closest to your desired value (here, 19) and do something like:

Link | Replyax.plot(np.ones(N)*X[0,19], Y[:,19], Z[:,19], lw=5, c='r', zorder=1000)

You'll need to set the zorder way up because of all those patches.

## New Comment