Multivariate Normals
Why
We generalize the normal density to
$d$-dimensional space.
Definition
Let $f: \R ^d \to \R $ be a density such that
\[
f(x) = \mgaussiandensity{x}{\mu }{\Sigma }
\]
where $\mu \in \R ^d$, $\Sigma \in
\mathbfsf{S} ^d$, and $\Sigma \succ 0$.
We call $f$ a multivariate
normal density.
A multivariate normal density with $d = 1$ is
a normal density, so we refer to multivariate
normal densities as normal
densities without ambiguity.
We frequently use the word normal as a
substantive, and refer to
normals when we mean
multivariate normal densities.
Many people call a multivariate normal
distribution a multivariate
gaussian distribution and speak of
gaussians instead of
normals.
We call $\mu $ the mean
and $\Sigma $ the covariance
matrix.
We call $\Sigma ^{-1}$ the
precision matrix.
Maximum
The maximum of a normal density is its mean,
$\mu \in \R ^d$.