abctools: An R Package for Tuning Approximate Bayesian Computation Analyses

Matthew A. Nunes, Dennis Prangle

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Approximate Bayesian computation (ABC) is a popular family of algorithms which perform approximate parameter inference when numerical evaluation of the likelihood function is not possible but data can be simulated from the model. They return a sample of parameter values which produce simulations close to the observed dataset. A standard approach is to reduce the simulated and observed datasets to vectors of summary statistics and accept when the difference between these is below a specified threshold. ABC can also be adapted to perform model choice. In this article, we present a new software package for R, abctools which provides methods for tuning ABC algorithms. This includes recent dimension reduction algorithms to tune the choice of summary statistics, and coverage methods to tune the choice of threshold. We provide several illustrations of these routines on applications taken from the ABC literature.
Original languageEnglish
Pages (from-to)189-205
Number of pages17
JournalThe R Journal
Volume7
Issue number2
Publication statusPublished - 29 Jul 2015

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Bayesian Computation
Tuning
Statistics
Model Choice
Dimension Reduction
Likelihood Function
Software Package
Coverage
Evaluation
Simulation

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abctools: An R Package for Tuning Approximate Bayesian Computation Analyses. / Nunes, Matthew A.; Prangle, Dennis.

In: The R Journal, Vol. 7, No. 2, 29.07.2015, p. 189-205.

Research output: Contribution to journalArticle

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