abctools:an R package for tuning Approximate Bayesian Computation analyses

Nunes, Matthew Alan and Prangle, Dennis (2015) abctools:an R package for tuning Approximate Bayesian Computation analyses. The R Journal, 7 (2). pp. 189-205. ISSN 2073-4859

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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.

Item Type:
Journal Article
Journal or Publication Title:
The R Journal
Additional Information:
The R Journal is a peer-reviewed publication of the R Foundation for Statistical Computing. Communications regarding this publication should be addressed to the editors. All articles are licensed under the Creative Commons Attribution 3.0 Unported license (CC BY 3.0, http://creativecommons.org/licenses/by/3.0/).
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
77619
Deposited By:
Deposited On:
06 Apr 2016 10:50
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Apr 2020 03:43