Is there any graphical explanation of Multivariate Gaussian? I am trying to catch up with my statistics. I want to understand what are multivariate Gaussian or multivariate normal distribution. I understand what a univariate Gaussian is any variable $X$ such that $X\sim N(\mu,\sigma^2),\mu\in\mathbb R, \sigma^2>0$ (except for complex numbers, isn't something squared always greater than $0$ ?), that is to say, if $x_i\in X\sim N(0,\sigma^2)$ There is lots of chance that it pertains to something that looks like this :

And as far as
if $x_i\in X\sim N(\mu,\sigma^2)$ with $\mu =\begin{bmatrix}0\\0\end{bmatrix}$ and $\sigma =\begin{bmatrix}1 & 3/5\\3/5 & 2\end{bmatrix}$ (I don't understand the  covariance matrix, especially the bottom right corner) It would have be part of something like this :

But then I don't understand its definition given on ... which I found back on Wikipedia :

A random vector $X = (X_1, …, X_k)'$ is said to have the multivariate normal distribution if it satisfies the following equivalent conditions.

*

*Every linear combination of its components $Y = a_1X_1 + … + a_kX_k$ is normally distributed
i.e. $a^TX=\sum_{i=1}^na_iX_i$ is Gaussian $\forall a\in\mathbb R^n$ (what $a$ has to do there ?)


Would it mean, if we had three random variables, that combining two of them would be a Gaussian, it would look like the picture above. Yet, it isn't a Gaussian, it's a bi dimensional Gaussian !
 A: No, the property means that if every projection from $k$ dimensions to $1$ (i.e. any linear combination) yields a normally distributed variable, then the $k$D variable is a multivariate Gaussian. [A single projection is insufficient. On your second plot, you see two different projections giving $1$D Gaussians. If I am right, $k$ independent projections are sufficient.]
If you want to picture a multivariate Gaussian, think of the locus of equiprobable points. In $1$D, you get two isolated points. In $2$, an ellipse. In $k$D, the generalization of an ellipse, i.e. an hyperellipsoid.
A: If you have three random variables $X_1,X_2,X_3$ then the statement that the triple $(X_1,X_2,X_3)$ has a multivariate Gaussian distribution means that regardless of which three numbers $a_1,a_2,a_3$ you choose, the linear combination $a_1X_1 + a_2X_2 + a_3X_3$ has a univariate Gaussian distribution. For example, $40X_1 + 5X_2 - 13X_3$ has a univariate Gaussian distribution, and similarly for any other triple of numbers besides $(40,5,-13).$
A: Multivariate as product of independent univariate gaussians
Correlated m=ultivariate gaussian in X space can be projected into de-correlated Y using the eigenvector matrix $U^T$ based on Eigen decomposition: Covariance of X $ = \Sigma = U\Lambda U^T$.
In Y space, the probability distribution is the product of independent univariate gaussian distributions.
$$p(y) \propto \prod\limits_{i=1}^D exp \Big( - \frac {1}{2} \sum_{i=1}^D \frac{y_i^2}{\lambda_i} \Big)$$
Devivation

I found it is helpful to understand the Mahalanobis Distance first.

