  Choice of appropriate structure and parametric dimension of a model in the
light of data has a rich history in statistical research, where the first
seminal approaches were developed in 1970s, such as the Akaike's and Schwarz's
model scoring criteria that were inspired by information theory and embodied
the rationale called Occam's razor. After those pioneering works, model choice
was quickly established as its own field of research, gaining considerable
attention in both computer science and statistics. However, to date, there have
been limited attempts to derive scoring criteria for simulator-based models
lacking a likelihood expression. Bayes factors have been considered for such
models, but arguments have been put both for and against use of them and around
issues related to their consistency. Here we use the asymptotic properties of
Jensen--Shannon divergence (JSD) to derive a consistent model scoring criterion
for the likelihood-free setting called JSD-Razor. Relationships of JSD-Razor
with established scoring criteria for the likelihood-based approach are
analyzed and we demonstrate the favorable properties of our criterion using
both synthetic and real modeling examples.

Methodology;Machine Learning