  Approximate Bayesian computation (ABC) has advanced in two decades from a
seminal idea to a practically applicable inference tool for simulator-based
statistical models, which are becoming increasingly popular in many research
domains. The computational feasibility of ABC for practical applications has
been recently boosted by adopting techniques from machine learning to build
surrogate models for the approximate likelihood or posterior and by the
introduction of a general-purpose software platform with several advanced
features, including automated parallelization. Here we demonstrate the
strengths of the advances in ABC by going beyond the typical benchmark examples
and considering real applications in astronomy, infectious disease
epidemiology, personalised cancer therapy and financial prediction. We
anticipate that the emerging success of ABC in producing actual added value and
quantitative insights in the real world will continue to inspire a plethora of
further applications across different fields of science, social science and
technology.
