Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models

Burr, Tom; Skurikhin, Alexei

doi:10.1155/2013/210646

Selecting Summary Statistics in Approximate Bayesian Computation for Calibrating Stochastic Models

Journal Article · Sun Sep 01 00:00:00 EDT 2013 · BioMed Research International

DOI:https://doi.org/10.1155/2013/210646· OSTI ID:1626210

Burr, Tom ^[1]; ^[2]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States); DOE/OSTI
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

OSTI ID:: 1626210

Journal Information:: BioMed Research International, Journal Name: BioMed Research International Vol. 2013; ISSN 2314-6133

Publisher:: HindawiCopyright Statement

Country of Publication:: United States

Language:: English

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Amount of Information Needed for Model Choice in Approximate Bayesian Computation Stocks, Michael; Siol, Mathieu; Lascoux, Martin PLoS ONE, Vol. 9, Issue 6 https://doi.org/10.1371/journal.pone.0099581	journal	June 2014
Estimating synchronous demographic changes across populations using hABC and its application for a herpetological community from northeastern Brazil Gehara, Marcelo; Garda, Adrian A.; Werneck, Fernanda P. Molecular Ecology, Vol. 26, Issue 18 https://doi.org/10.1111/mec.14239	journal	August 2017

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