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ABC random forests for Bayesian parameter inference
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Raynal, Louis, Marin, Jean-Michel, Pudlo, Pierre, Ribatet, Mathieu, Robert, Christian P. and Estoup, Arnaud (2019) ABC random forests for Bayesian parameter inference. Bioinformatics, 35 (10). pp. 1720-1728. doi:10.1093/bioinformatics/bty867 ISSN 1367-4803.
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Official URL: https://doi.org/10.1093/bioinformatics/bty867
Abstract
Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of informative statistics summarizing raw data. Furthermore, in almost all existing implementations, the tolerance level that separates acceptance from rejection of simulated parameter values needs to be calibrated. We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level. The approach relies on the random forest methodology of Breiman (2001) applied in a (non parametric) regression setting. We advocate the derivation of a new random forest for each component of the parameter vector of interest. When compared with earlier ABC solutions, this method offers significant gains in terms of robustness to the choice of the summary statistics, does not depend on any type of tolerance level, and is a good trade-off in term of quality of point estimator precision and credible interval estimations for a given computing time. We illustrate the performance of our methodological proposal and compare it with earlier ABC methods on a Normal toy example and a population genetics example dealing with human population evolution.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics Q Science > QH Natural history |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) > Biological Sciences ( -2010) Faculty of Science, Engineering and Medicine > Science > Centre for Scientific Computing Faculty of Science, Engineering and Medicine > Science > Statistics |
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Library of Congress Subject Headings (LCSH): | Population genetics -- Mathematical models, Bayesian statistical decision theory | |||||||||||||||
Journal or Publication Title: | Bioinformatics | |||||||||||||||
Publisher: | Oxford University Press | |||||||||||||||
ISSN: | 1367-4803 | |||||||||||||||
Official Date: | 15 May 2019 | |||||||||||||||
Dates: |
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Volume: | 35 | |||||||||||||||
Number: | 10 | |||||||||||||||
Page Range: | pp. 1720-1728 | |||||||||||||||
DOI: | 10.1093/bioinformatics/bty867 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record Louis Raynal, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet, Christian P Robert, Arnaud Estoup; ABC random forests for Bayesian parameter inference, Bioinformatics, , bty867, https://doi.org/10.1093/bioinformatics/bty867 is available online at: https://doi.org/10.1093/bioinformatics/bty867 | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Date of first compliant deposit: | 1 October 2018 | |||||||||||||||
Date of first compliant Open Access: | 15 October 2019 | |||||||||||||||
RIOXX Funder/Project Grant: |
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