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.

翻译：近似贝叶斯计算(ABC)在过去二十年中从一个基本想法发展到一个实际适用的模拟基于统计模型的推论工具,这种推论工具在许多研究领域越来越受欢迎;ABC用于实际应用的计算可行性最近得到提高,其方法是采用机器学习技术,为近似可能性或后台建立代用模型,以及采用具有包括自动化平行化在内的若干先进特征的通用软件平台;我们在这里展示ABC进步的长处,超越典型的基准范例,考虑天文学、传染病流行病学、个性化癌症治疗和金融预测的实际应用;我们预计ABC在现实世界中产生实际附加值和定量洞察力方面正在取得的成功,将继续激发科学、社会科学和技术不同领域的大量进一步应用。