Title *

ABC random forests for Bayesian parameter inferenceuse asterix (*) to get italics

Authors *

Louis Raynal, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet, Christian P. Robert, Arnaud EstoupPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

Year *

2017

Picture *

Abstract *

<p>This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http:// dx.doi.org/ 10.24072/ pci.evolbiol.100036). 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 infor- mative statistics summarizing raw data. Furthermore, in almost all existing implementations, the toler- ance 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 toler- ance 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 sig- nificant 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. All methods designed here have been incorporated in the R package abcrf (version 1.7) available on CRAN.</p>

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Keywords (optional)

Approximate Bayesian computation, Bayesian inference, likelihood-free methods, parameter inference, random forests

Methods that require specific expertise (optional)

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Thematic fields *

Bioinformatics & Computational Biology, Evolutionary Applications, Other, Population Genetics / Genomics

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Submission date

2017-07-06 07:42:00

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