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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - abctools
T2 - an R package for tuning Approximate Bayesian Computation analyses
AU - Nunes, Matthew Alan
AU - Prangle, Dennis
N1 - 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/).
PY - 2015/12
Y1 - 2015/12
N2 - Approximate Bayesian Computation (ABC) is a popular family of algorithms which performapproximate 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 fortuning ABC algorithms. This includes recent dimension reduction algorithms to tune the choiceof summary statistics, and coverage methods to tune the choice of threshold. We provide severalillustrations of these routines on applications taken from the ABC literature.
AB - Approximate Bayesian Computation (ABC) is a popular family of algorithms which performapproximate 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 fortuning ABC algorithms. This includes recent dimension reduction algorithms to tune the choiceof summary statistics, and coverage methods to tune the choice of threshold. We provide severalillustrations of these routines on applications taken from the ABC literature.
M3 - Journal article
VL - 7
SP - 189
EP - 205
JO - The R Journal
JF - The R Journal
SN - 2073-4859
IS - 2
ER -