Approximate Bayesian computation

Many modern statistical settings feature the analysis of data that may arise from unknown generating processes, or processes for which the generative models are computationally infeasible to interact with. Conventional estimation and inference solution methods in such settings may be unwieldy or impossible to implement. The approximate Bayesian computation (ABC) approach is a potent method in such scenarios, since it does not require the knowledge of the underlying generative model in order to perform inference. Furthermore, when combined with sufficiently regular discrepancy measurements such as the energy statistic, ABC can be shown to have desirable asymptotic properties. We provide a concise introduction to the general ABC framework.