Cargando…

Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models

Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics...

Descripción completa

Detalles Bibliográficos
Autores principales: Burr, Tom, Skurikhin, Alexei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3830866/
https://www.ncbi.nlm.nih.gov/pubmed/24288668
http://dx.doi.org/10.1155/2013/210646
_version_ 1782291539354976256
author Burr, Tom
Skurikhin, Alexei
author_facet Burr, Tom
Skurikhin, Alexei
author_sort Burr, Tom
collection PubMed
description Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the full data and is therefore computationally feasible. One technical challenge with ABC is that the quality of the approximation to the posterior distribution of model parameters depends on the user-chosen summary statistics. In this paper, the user requirement to choose effective summary statistics in order to accurately estimate the posterior distribution of model parameters is investigated and illustrated by example, using a model and corresponding real data of mitochondrial DNA population dynamics. We show that for some choices of summary statistics, the posterior distribution of model parameters is closely approximated and for other choices of summary statistics, the posterior distribution is not closely approximated. A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example.
format Online
Article
Text
id pubmed-3830866
institution National Center for Biotechnology Information
language English
publishDate 2013
publisher Hindawi Publishing Corporation
record_format MEDLINE/PubMed
spelling pubmed-38308662013-11-28 Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models Burr, Tom Skurikhin, Alexei Biomed Res Int Research Article Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the full data and is therefore computationally feasible. One technical challenge with ABC is that the quality of the approximation to the posterior distribution of model parameters depends on the user-chosen summary statistics. In this paper, the user requirement to choose effective summary statistics in order to accurately estimate the posterior distribution of model parameters is investigated and illustrated by example, using a model and corresponding real data of mitochondrial DNA population dynamics. We show that for some choices of summary statistics, the posterior distribution of model parameters is closely approximated and for other choices of summary statistics, the posterior distribution is not closely approximated. A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example. Hindawi Publishing Corporation 2013 2013-09-01 /pmc/articles/PMC3830866/ /pubmed/24288668 http://dx.doi.org/10.1155/2013/210646 Text en Copyright © 2013 T. Burr and A. Skurikhin. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Burr, Tom
Skurikhin, Alexei
Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models
title Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models
title_full Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models
title_fullStr Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models
title_full_unstemmed Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models
title_short Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models
title_sort selecting summary statistics in approximate bayesian computation for calibrating stochastic models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3830866/
https://www.ncbi.nlm.nih.gov/pubmed/24288668
http://dx.doi.org/10.1155/2013/210646
work_keys_str_mv AT burrtom selectingsummarystatisticsinapproximatebayesiancomputationforcalibratingstochasticmodels
AT skurikhinalexei selectingsummarystatisticsinapproximatebayesiancomputationforcalibratingstochasticmodels