  Bayesian model selection with improper priors is not well-defined because of
the dependence of the marginal likelihood on the arbitrary scaling constants of
the within-model prior densities. We show how this problem can be evaded by
replacing marginal log-likelihood by a homogeneous proper scoring rule, which
is insensitive to the scaling constants. Suitably applied, this will typically
enable consistent selection of the true model.
