 Conditional regression coefficients from a Gaussian precision matrix

In a Gaussian graphical model, regressing component $k$ on the others gives the displayed coefficients, residual variance $1/{\Omega }_{kk}$ and intercept ${\mu }_k-{\mu }_{-k}^{\mathsf{T}}{\theta }_{-k,k}$. Opposite directions have identical zero patterns and signs because $\Omega$ is symmetric with positive diagonal, but generally unequal coefficient magnitudes.