ABC of the future
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2023-08
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
International Statistical Review, Volume 91, issue 2, pp. 243-268
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 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.Description
Funding Information: This work was supported by the European Research Council grant number 742158, European Union's Horizon 2020 Research and Innovation Programme, Grant Agreement number 847912, Research Council of Norway, Grant Numbers 237718, 311188 and 309273, the Academy of Finland grant number 1316602, Australian Research Council Discovery Grants DP170100729 and DP200101414 and Australian Research Council Early Career Researcher Award DE200101070.
Keywords
approximate Bayesian computation, Bayesian inference, likelihood-free inference, simulator-based inference
Other note
Citation
Pesonen, H, Simola, U, Köhn-Luque, A, Vuollekoski, H, Lai, X, Frigessi, A, Kaski, S, Frazier, D T, Maneesoonthorn, W, Martin, G M & Corander, J 2023, ' ABC of the future ', International Statistical Review, vol. 91, no. 2, pp. 243-268 . https://doi.org/10.1111/insr.12522