  Increasingly complex generative models are being used across disciplines as
they allow for realistic characterization of data, but a common difficulty with
them is the prohibitively large computational cost to evaluate the likelihood
function and thus to perform likelihood-based statistical inference. A
likelihood-free inference framework has emerged where the parameters are
identified by finding values that yield simulated data resembling the observed
data. While widely applicable, a major difficulty in this framework is how to
measure the discrepancy between the simulated and observed data. Transforming
the original problem into a problem of classifying the data into simulated
versus observed, we find that classification accuracy can be used to assess the
discrepancy. The complete arsenal of classification methods becomes thereby
available for inference of intractable generative models. We validate our
approach using theory and simulations for both point estimation and Bayesian
inference, and demonstrate its use on real data by inferring an
individual-based epidemiological model for bacterial infections in child care
centers.
