In this section
@article{Barnes:2012,
author = {Barnes, C and Filippi, S and Stumpf, MPH and Thorne, T},
journal = {Statistics and Computing},
pages = {1181--1197},
title = {Considerate approaches to achieving sufficiency for ABC model selection},
url = {http://hdl.handle.net/10044/1/47892},
volume = {22},
year = {2012}
}
TY - JOUR
AB - For nearly any challenging scientific problemevaluation of the likelihood is problematic if not impossible.Approximate Bayesian computation (ABC) allowsus to employ the whole Bayesian formalism to problemswhere we can use simulations from a model, but cannotevaluate the likelihood directly. When summary statistics ofreal and simulated data are compared—rather than the datadirectly—information is lost, unless the summary statisticsare sufficient. Sufficient statistics are, however, not commonbut without them statistical inference in ABC inferencesare to be considered with caution. Previously other authorshave attempted to combine different statistics in order toconstruct (approximately) sufficient statistics using searchand information heuristics. Here we employ an informationtheoreticalframework that can be used to construct appropriate(approximately sufficient) statistics by combining differentstatistics until the loss of information is minimized.We start from a potentially large number of different statisticsand choose the smallest set that captures (nearly) thesame information as the complete set. We then demonstratethat such sets of statistics can be constructed for both parameterestimation and model selection problems, and we applyour approach to a range of illustrative and real-world modelselection problems.
AU - Barnes,C
AU - Filippi,S
AU - Stumpf,MPH
AU - Thorne,T
EP - 1197
PY - 2012///
SN - 0960-3174
SP - 1181
TI - Considerate approaches to achieving sufficiency for ABC model selection
T2 - Statistics and Computing
UR - http://hdl.handle.net/10044/1/47892
VL - 22
ER -