Methods for the identification of summary statistics that could also simultaneously assess the influence on the approximation of the posterior would be of substantial value. This is because the choice of summary statistics and the choice of tolerance constitute two sources of error in the resulting posterior distribution. These errors may corrupt the ranking of models and may also lead to incorrect model predictions. Indeed, none of the methods above assesses the choice of summaries for the purpose of model selection.