ABC of the Future

TL;DR

This paper reviews recent advances in Approximate Bayesian Computation (ABC), highlighting its practical applications across diverse fields and the impact of machine learning techniques and software tools in enhancing its feasibility.

Contribution

It demonstrates the application of modern ABC techniques to real-world problems beyond benchmarks, emphasizing its growing utility in various scientific domains.

Findings

ABC has become practically applicable in multiple research fields.

Machine learning techniques improve ABC computational efficiency.

ABC provides valuable insights in astronomy, epidemiology, cancer therapy, and finance.

Abstract

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.