abctools: an R package for tuning approximate Bayesian computation analyses

Nunes, M. A. and Prangle, D. (2015) abctools: an R package for tuning approximate Bayesian computation analyses. R Journal, 7 (2). pp. 189-205. ISSN 2073-4859

Abstract/Summary

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	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/55506
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Publisher	R Foundation
Download/View statistics	View download statistics for this item