  We recently proposed a general algorithm for approximating nonstandard
Bayesian posterior distributions by minimization of their Kullback-Leibler
divergence with respect to a more convenient approximating distribution. In
this note we offer details on how to efficiently implement this algorithm in
practice. We also suggest default choices for the form of the posterior
approximation, the number of iterations, the step size, and other user choices.
By using these defaults it becomes possible to construct good posterior
approximations for hierarchical models completely automatically.
