  We propose a novel approach to model selection for simulator-based
statistical models. The proposed approach defines a mixture of candidate
models, and then iteratively updates the weight coefficients for those models
as well as the parameters in each model simultaneously; this is done by
recursively applying Bayes' rule, using the recently proposed kernel recursive
ABC algorithm. The practical advantage of the method is that it can be used
even when a modeler lacks appropriate prior knowledge about the parameters in
each model. We demonstrate the effectiveness of the proposed approach with a
number of experiments, including model selection for dynamical systems in
ecology and epidemiology.
