Show simple item record

dc.contributor.authorPudlo, Pierre*
dc.contributor.authorMarin, Jean-Michel*
dc.contributor.authorEstoup, Arnaud*
dc.contributor.authorCornuet, Jean-Marie*
dc.contributor.authorGautier, Mathieu*
dc.contributor.authorRobert, Christian P.*
dc.date.accessioned2014-07-17T08:19:28Z
dc.date.available2014-07-17T08:19:28Z
dc.date.issued2016
dc.identifier.issn1367-4803
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/13733
dc.language.isoenen
dc.subjectsummary statistics
dc.subjectBayesian model choice
dc.subjectbagging
dc.subjectk -nearest neighbors
dc.subjectHarlequin ladybird
dc.subjectlikelihood-free methods
dc.subjectposterior predictive,
dc.subjectrandom forests
dc.subjectsparsity
dc.subjecterror rate
dc.subjectsubsampling
dc.subjectbootstrap
dc.subjectmodel selection
dc.subjectApproximate Bayesian computation
dc.subject.ddc519en
dc.subject.classificationjelC52en
dc.subject.classificationjelC11en
dc.titleReliable ABC model choice via random forests
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherCBGP, INRA,;France
dc.contributor.editoruniversityotherInstitut de Biologie Computationnelle (IBC),;France
dc.contributor.editoruniversityotherI3M, Université de Montpellier 2,;France
dc.description.abstractenMotivation: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques.Results: We propose a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with RF and postponing the approximation of the posterior probability of the selected model for a second stage also relying on RF. Compared with earlier implementations of ABC model choice, the ABC RF approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least 50) and (iv) it includes an approximation of the posterior probability of the selected model. The call to RF will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets.Availability and implementation: The proposed methodology is implemented in the R package abcrf available on the CRAN.Contact: jean-michel.marin@umontpellier.frSupplementary information: Supplementary data are available at Bioinformatics online.
dc.publisher.cityParisen
dc.relation.isversionofjnlnameBioinformatics
dc.relation.isversionofjnlvol32
dc.relation.isversionofjnlissue6
dc.relation.isversionofjnldate2016
dc.relation.isversionofjnlpages859-866
dc.relation.isversionofdoi10.1093/bioinformatics/btv684
dc.identifier.urlsitehttps://arxiv.org/abs/1406.6288v3
dc.relation.isversionofjnlpublisherOxford University Press
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.description.submittednonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2017-01-03T11:25:28Z
hal.person.labIds*
hal.person.labIds*
hal.person.labIds*
hal.person.labIds*
hal.person.labIds*
hal.person.labIds*


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record