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ABC of the future
Author(s) -
Pesonen Henri,
Simola Umberto,
KöhnLuque Alvaro,
Vuollekoski Henri,
Lai Xiaoran,
Frigessi Arnoldo,
Kaski Samuel,
Frazier David T.,
Maneesoonthorn Worapree,
Martin Gael M.,
Corander Jukka
Publication year - 2023
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12522
Subject(s) - approximate bayesian computation , computer science , benchmark (surveying) , data science , inference , bayesian inference , machine learning , computation , bayesian probability , artificial intelligence , algorithm , geodesy , geography
Summary 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.