  Some statistical models are specified via a data generating process for which
the likelihood function cannot be computed in closed form. Standard
likelihood-based inference is then not feasible but the model parameters can be
inferred by finding the values which yield simulated data that resemble the
observed data. This approach faces at least two major difficulties: The first
difficulty is the choice of the discrepancy measure which is used to judge
whether the simulated data resemble the observed data. The second difficulty is
the computationally efficient identification of regions in the parameter space
where the discrepancy is low. We give here an introduction to our recent work
where we tackle the two difficulties through classification and Bayesian
optimization.
