  Moment methods are classical approaches that approximate the mesoscopic
radiative transfer equation by a system of macroscopic moment equations. An
expansion in the angular variables transforms the original equation into a
system of infinitely many moments. The truncation of this infinite system is
the moment closure problem. Many types of closures have been presented in the
literature. In this note, we demonstrate that optimal prediction, an approach
originally developed to approximate the mean solution of systems of nonlinear
ordinary differential equations, can be used to derive moment closures. To that
end, the formalism is generalized to systems of partial differential equations.
Using Gaussian measures, existing linear closures can be re-derived, such as
$P_N$, diffusion, and diffusion correction closures. This provides a new
perspective on several approximations done in the process and gives rise to
ideas for modifications to existing closures.
