Approximate Bayesian Computation for Real Applications
ABC of the Future
Approximate bayesian computation has advanced in two decades from a seminal idea to a practically applicable inference tool for simulator-basedstatistical models, which are becoming increasingly popular in many research domains.
The computational feasibility of approximate bayesian computation 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 approximate bayesian computation by going beyond the typical benchmark examples and considering real applications in astronomy, infectious disease epidemiology, personalised cancer therapy and financial prediction.
Henri Pesonen, Umberto Simola, Alvaro Köhn-Luque, Henri Vuollekoski, Xiaoran Lai, Arnoldo Frigessi, Samuel Kaski, David T. Frazier, Worapree Maneesoonthorn, Gael M. Martin, Jukka Corander