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An automatic adaptive method to combine summary statistics in approximate Bayesian computation

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Harrison, Jonathan and Baker, Ruth E. (2020) An automatic adaptive method to combine summary statistics in approximate Bayesian computation. PLoS One, 15 (8). e0236954. doi:10.1371/journal.pone.0236954 ISSN 1932-6203.

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Official URL: https://doi.org/10.1371/journal.pone.0236954

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Abstract

To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is that parameter inference must generally rely on summary statistics of the data. This is particularly the case for problems involving high-dimensional data, such as biological imaging experiments. However, some summary statistics contain more information about parameters of interest than others, and it is not always clear how to weight their contributions within the ABC framework. We address this problem by developing an automatic, adaptive algorithm that chooses weights for each summary statistic. Our algorithm aims to maximize the distance between the prior and the approximate posterior by automatically adapting the weights within the ABC distance function. Computationally, we use a nearest neighbour estimator of the distance between distributions. We justify the algorithm theoretically based on properties of the nearest neighbour distance estimator. To demonstrate the effectiveness of our algorithm, we apply it to a variety of test problems, including several stochastic models of biochemical reaction networks, and a spatial model of diffusion, and compare our results with existing algorithms.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Mathematics
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Bayesian statistical decision theory, Mathematical analysis
Journal or Publication Title: PLoS One
Publisher: Public Library of Science
ISSN: 1932-6203
Official Date: 6 August 2020
Dates:
DateEvent
6 August 2020Published
16 July 2020Accepted
Volume: 15
Number: 8
Article Number: e0236954
DOI: 10.1371/journal.pone.0236954
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 13 August 2020
Date of first compliant Open Access: 18 August 2020
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/ G03706X/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
Wolfson ResearchMerit AwardRoyal Societyhttp://dx.doi.org/10.13039/501100000288
UNSPECIFIEDLeverhulme Trusthttp://dx.doi.org/10.13039/501100000275
BB/R00816/1[BBSRC] Biotechnology and Biological Sciences Research Councilhttp://dx.doi.org/10.13039/501100000268
Contributors:
ContributionNameContributor ID
UNSPECIFIEDMariño, Inés P.UNSPECIFIED

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