Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 43 / 1 / i

Write the expected value as ${\mu }^T=({\mu }_1^T,{\mu }_2^T)$ and assume the relevant covariance matrix $V_{22}$ is invertible. Remove the linear predictor from the first block by definingA linear image of a multivariate normal vector is again multivariate normal. Direct covariance calculation givesThus $Y$ and $X_2$ are independent: uncorrelated jointly Gaussian variables are independent. Expanding the covariance matrix of $Y$, using $V_{21}=V_{12}^T$, givesConditioning on $X_2=x_2$ translates $Y$ by the fixed vector ${\mu }_1+V_{12}{V}_{22}^{-1}(x_2-{\mu }_2)$ and leaves its covariance matrix unchanged. Consequently the conditional multivariate normal distribution isThe conditional covariance is the Schur complement and does not depend on $x_2$.