  Model averaging is a useful and robust method for dealing with model
uncertainty in statistical analysis. Often, it is useful to consider data
subset selection at the same time, in which model selection criteria are used
to compare models across different subsets of the data. Two different criteria
have been proposed in the literature for how the data subsets should be
weighted. We compare the two criteria closely in a unified treatment based on
the Kullback-Leibler divergence, and conclude that one of them is subtly flawed
and will tend to yield larger uncertainties due to loss of information.
Analytical and numerical examples are provided.

Methodology;High Energy Physics - Lattice