  Until recently, the use of Bayesian inference in population genetics was
limited to a few cases because for many realistic population genetic models the
likelihood function cannot be calculated analytically . The situation changed
with the advent of likelihood-free inference algorithms, often subsumed under
the term Approximate Bayesian Computation (ABC). A key innovation was the use
of a post-sampling regression adjustment, allowing larger tolerance values and
as such shifting computation time to realistic orders of magnitude (see
Beaumont et al., 2002). Here we propose a reformulation of the regression
adjustment in terms of a General Linear Model (GLM). This allows the
integration into the framework of Bayesian statistics and the use of its
methods, including model selection via Bayes factors. We then apply the
proposed methodology to the question of population subdivision among western
chimpanzees Pan troglodytes verus.
