ABC of the future

Research output: Contribution to conferencePaperpeer-review


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 parallelisation. 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.
Original languageEnglish
Number of pages26
Publication statusPublished - Jan 2023


  • Machine learning
  • Bayesian modeling
  • Cancer therapy

Research Beacons, Institutes and Platforms

  • Digital Futures
  • Institute for Data Science and AI
  • Christabel Pankhurst Institute
  • Cancer


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