Put univariate and multivariate Gaussians together for comparison:

Univariate Gaussian:

\[\begin{align} p(x) &= \frac{1}{\sqrt{2 \pi \sigma^2}} \exp \left \{- \frac{1}{2} \frac{(x - \mu)^2}{\sigma^2} \right \} \\ &= \frac{1}{\sqrt{2 \pi \sigma^2}} \exp \left \{- \frac{1}{2} (x - \mu)\frac{1}{\sigma^2}(x - \mu) \right \} \\ \end{align}\]

Multivariate Gaussian:

\[\begin{equation} p(\mathbf{x}) = \frac{1}{\sqrt{(2 \pi)^D |\Sigma|}} \exp \left \{ - \frac{1}{2} (\mathbf{x} - \boldsymbol{\mu})^T \Sigma^{-1} (\mathbf{x} - \boldsymbol{\mu}) \right \} \end{equation}\]

If in terms of precision ($\Lambda$):

\[\begin{equation} p(\mathbf{x}) = (2\pi)^{-\frac{D}{2}} |\Lambda|^{\frac{1}{2}} \exp \left \{ - \frac{1}{2} (\mathbf{x} - \boldsymbol{\mu})^T \Lambda(\mathbf{x} - \boldsymbol{\mu}) \right \} \end{equation}\]

Conditional distributions

Given

\[\begin{align} \mathbf{x} &= \begin{bmatrix} \mathbf{x}_a \\ \mathbf{x}_b \end{bmatrix} \\ \boldsymbol{\mu} &= \begin{bmatrix} \boldsymbol{\mu}_a \\ \boldsymbol{\mu}_b \end{bmatrix} \\ \Sigma &= \begin{bmatrix} \Sigma_{aa} & \Sigma_{ab} \\ \Sigma_{ba} & \Sigma_{bb} \end{bmatrix} \\ \Lambda &= \begin{bmatrix} \Lambda_{aa} & \Lambda_{ab} \\ \Lambda_{ba} & \Lambda_{bb} \end{bmatrix} = \Sigma^{-1} \\ \end{align}\]

Note, $\Sigma$ is the covariance matrix and $\Lambda$ is its inverse, called the precision matrix. Also note that the partitioned matrices of $\Sigma$ and $\Lambda$ are note inverse of each other, i.e. \(\Sigma_{ij} \ne \Lambda_{ij}^{-1}\).

After some algebra, the conditional mean and variance expressed in terms of partitioned precision matrices:

\[\begin{align} \boldsymbol{\mu}_{a|b} &= \boldsymbol{\mu}_a - \Lambda_{aa}^{-1} \Lambda_{ab} (\mathbf{x}_b - \boldsymbol{\mu}_b) \\ \Sigma_{a|b} &= \Lambda_{aa}^{-1} \\ \end{align}\]

Alternatively, these can be also expressed in terms of partitioned covariance matrices, which is a bit more complex for \(\Sigma_{a|b}\):

\[\begin{align} \boldsymbol{\mu}_{a|b} &= \boldsymbol{\mu}_a + \Sigma_{ab} \Sigma_{bb}^{-1} (\mathbf{x}_b - \boldsymbol{\mu}_b) \\ \Sigma_{a|b} &= \Sigma_{aa} - \Sigma_{ab} \Sigma_{aa}^{-1} \Sigma_{ba} \end{align}\]

Note \(\boldsymbol{\mu}_{a|b}\) is a linear function of \(\mathbf{x}_b\).

so \(p(\mathbf{x}_a|\mathbf{x}_b) \sim \mathcal{N}(\boldsymbol{\mu}_{a|b},\Sigma_{a|b})\).

Marginal distribution

\[\begin{align} \mathbb{E}[\mathbf{x}_a] &= \boldsymbol{\mu}_a \\ \text{cov}[\mathbf{x}_a] &= \Sigma_{aa} \\ \end{align}\]

i.e. \(p(\mathbf{x}_a) \sim \mathcal{N}(\mathbf{x}_a | \boldsymbol{\mu}_a, \Sigma_{aa})\), which is straightforward.

Conjugate prior for Gaussian distributions with different unknown parameters:

mean variance/precision dimension conjugate prior
unknown known univariate univarite Gaussian distribution
unknown known multivariate multivariate Gaussian distribution
known unknown univariate Gamma distribution
known unknown multivariate Wishart distribution (Multivariate gamma distribution)
unknown unknown univariate Gaussian-gamma distribution
unknown unknown multivariate Gaussian-Wishart distribution


Maximum-likelihood estimates

See this notebook.