Reliable ABC model choice via random forests
Pudlo, Pierre; Marin, Jean-Michel; Estoup, Arnaud; Cornuet, Jean-Marie; Gautier, Mathieu; Robert, Christian P. (2016), Reliable ABC model choice via random forests, Bioinformatics, 32, 6, p. 859-866. 10.1093/bioinformatics/btv684
TypeArticle accepté pour publication ou publié
Lien vers un document non conservé dans cette basehttps://arxiv.org/abs/1406.6288v3
Nom de la revueBioinformatics
MétadonnéesAfficher la notice complète
Robert, Christian P.
Résumé (EN)Motivation: 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: firstname.lastname@example.orgSupplementary information: Supplementary data are available at Bioinformatics online.
Mots-cléssummary statistics; Bayesian model choice; bagging; k -nearest neighbors; Harlequin ladybird; likelihood-free methods; posterior predictive,; random forests; sparsity; error rate; subsampling; bootstrap; model selection; Approximate Bayesian computation
Affichage des éléments liés par titre et auteur.
Raynal, Louis; Marin, Jean-Michel; Pudlo, Pierre; Ribatet, Mathieu; Robert, Christian P.; Estoup, Arnaud (2019) Article accepté pour publication ou publié
Estimation of demo-genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics. Cornuet, Jean-Marie; Robert, Christian P.; Pudlo, Pierre; Guillemaud, Thomas; Marin, Jean-Michel; Lombaert, Eric; Estoup, Arnaud (2012) Article accepté pour publication ou publié
Infering population history with DIY ABC : a user-friendly approach to Approximate Bayesian Computation Estoup, Arnaud; Marin, Jean-Michel; Robert, Christian P.; Beaumont, Mark A.; Santos, Filipe; Guillemaud, Thomas; Balding, David; Cornuet, Jean-Marie (2008-04) Article accepté pour publication ou publié