  In scientific inference problems, the underlying statistical modeling
assumptions have a crucial impact on the end results. There exist, however,
only a few automatic means for validating these fundamental modelling
assumptions. The contribution in this paper is a general criterion to evaluate
the consistency of a set of statistical models with respect to observed data.
This is achieved by automatically gauging the models' ability to generate data
that is similar to the observed data. Importantly, the criterion follows from
the model class itself and is therefore directly applicable to a broad range of
inference problems with varying data types, ranging from independent univariate
data to high-dimensional time-series. The proposed data consistency criterion
is illustrated, evaluated and compared to several well-established methods
using three synthetic and two real data sets.
