# Conditional covariance of a multivariate normal vector

We know that the conditional variance of a multivariate normal vector $$(X,Y)$$ is the Schur complement: $$V(X|Y=(y_1,...,y_n))=\Sigma_{XX}-\Sigma_{XY}\Sigma_{YY}^{-1}\Sigma_{YX}$$

I have the intuition that the conditional covariance has the same form but I don't know how to prove it: $$Cov(X_1,X_2|Y=(y_1,...,y_n))=\Sigma_{X_1X_2}-\Sigma_{X_1Y}\Sigma_{YY}^{-1}\Sigma_{YX_2}$$ Where $$Cov(X_1,X_2|Y)=E(X_1X_2|Y)-E(X_1|Y)E(X_2|Y)$$.

Does someone know how to prove it?

Thank you so much.

Since the first equation is true for any $$X$$ vector, we can define $$X=\begin{bmatrix}X_1 \\ X_2\end{bmatrix}$$, and $$V(X|Y)$$ will be $$\begin{bmatrix}V(X_1,X_1|Y) & V(X_1,X_2|Y) \\ V(X_2,X_1|Y) & V(X_2,X_2|Y)\end{bmatrix}$$. We want the upper right corner of this matrix. Also, we can define $$\Sigma_{XX}=\begin{bmatrix}\Sigma_{X_1,X_1} & \Sigma_{X_1,X_2} \\ \Sigma_{X_2,X_1} & \Sigma_{X_2,X_2}\end{bmatrix}$$ and $$\Sigma_{XY}=\begin{bmatrix}\Sigma_{X_1,Y} \\ \Sigma_{X_2,Y}\end{bmatrix}$$, $$\Sigma_{YX}=\begin{bmatrix}\Sigma_{X_1,Y} & \Sigma_{X_2,Y}\end{bmatrix}$$. Substituting these into the first equation yields: \begin{align}V(X|Y) & =\begin{bmatrix}\Sigma_{X_1,X_1} & \Sigma_{X_1,X_2} \\ \Sigma_{X_2,X_1} & \Sigma_{X_2,X_2}\end{bmatrix}-\begin{bmatrix}\Sigma_{X_1,Y} \\ \Sigma_{X_2,Y}\end{bmatrix}\Sigma_{YY}^{-1}\begin{bmatrix}\Sigma_{X_1,Y} & \Sigma_{X_2,Y}\end{bmatrix} \\ & = \begin{bmatrix}\Sigma_{X_1,X_1} & \Sigma_{X_1,X_2} \\ \Sigma_{X_2,X_1} & \Sigma_{X_2,X_2}\end{bmatrix}-\begin{bmatrix}\Sigma_{X_1Y}\Sigma_{YY}^{-1}\Sigma_{YX_1} & \Sigma_{X_1Y}\Sigma_{YY}^{-1}\Sigma_{YX_2} \\ \Sigma_{X_2Y}\Sigma_{YY}^{-1}\Sigma_{YX_1} & \Sigma_{X_2Y}\Sigma_{YY}^{-1}\Sigma_{YX_2}\end{bmatrix}\end{align}. The upper right corner is $$\Sigma_{X_1,X_2}-\Sigma_{X_1Y}\Sigma_{YY}^{-1}\Sigma_{YX_2}$$ just as your wrote.
• Thank you so much gunes. This trick is really helpful. Just one issue that puzzles me: $\Sigma_{XX}-\Sigma_{XY}\Sigma_{YY}^{-1}\Sigma_{YX}$ is a scalar matrix, not a random variable. Therefore, the Schur complement is not equal to the conditional variance $V(X|Y)$ (which is random) but rather to the variance of X conditional to a certain realization of Y $V(X|Y=(y_1,...,y_n))$, isn't it? If so, what is this $(y_1,...,y_n)$? Commented Jan 13, 2019 at 13:42