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Some statistical models are specified via a data generating process for whichthe likelihood function cannot be computed in closed form. Standardlikelihood-based inference is then not feasible but the model parameters can beinferred by finding the values which yield simulated data that resemble theobserved data. This approach faces at least two major difficulties: The firstdifficulty is the choice of the discrepancy measure which is used to judgewhether the simulated data resemble the observed data. The second difficulty isthe computationally efficient identification of regions in the parameter spacewhere the discrepancy is low. We give here an introduction to our recent workwhere we tackle the two difficulties through classification and Bayesianoptimization.

Classification and Bayesian Optimization for Likelihood-Free Inference