Approximate Bayesian computational methods JM Marin, P Pudlo, CP Robert, RJ Ryder Statistics and Computing 22 (6), 1167-1180, 2012 | 595 | 2012 |

DIYABC v2. 0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data JM Cornuet, P Pudlo, J Veyssier, A Dehne-Garcia, M Gautier, R Leblois, ... Bioinformatics 30 (8), 1187-1189, 2014 | 545 | 2014 |

The effect of RAD allele dropout on the estimation of genetic variation within and between populations M Gautier, K Gharbi, T Cezard, J Foucaud, C Kerdelhué, P Pudlo, ... Molecular ecology 22 (11), 3165-3178, 2013 | 199 | 2013 |

Reliable ABC model choice via random forests P Pudlo, JM Marin, A Estoup, JM Cornuet, M Gautier, CP Robert Bioinformatics 32 (6), 859-866, 2015 | 138 | 2015 |

Estimation of population allele frequencies from next‐generation sequencing data: pool‐versus individual‐based genotyping M Gautier, J Foucaud, K Gharbi, T Cézard, M Galan, A Loiseau, ... Molecular Ecology 22 (14), 3766-3779, 2013 | 120 | 2013 |

Estimation of demo‐genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics A Estoup, E Lombaert, JM Marin, T Guillemaud, P Pudlo, CP Robert, ... Molecular Ecology Resources 12 (5), 846-855, 2012 | 91 | 2012 |

Bayesian computation via empirical likelihood KL Mengersen, P Pudlo, CP Robert Proceedings of the National Academy of Sciences 110 (4), 1321-1326, 2013 | 62 | 2013 |

Deciphering the Routes of invasion of *Drosophila suzukii* by Means of ABC Random ForestA Fraimout, V Debat, S Fellous, RA Hufbauer, J Foucaud, P Pudlo, ... Molecular biology and evolution 34 (4), 980-996, 2017 | 55 | 2017 |

Maximum-likelihood inference of population size contractions from microsatellite data R Leblois, P Pudlo, J Néron, F Bertaux, C Reddy Beeravolu, R Vitalis, ... Molecular biology and evolution 31 (10), 2805-2823, 2014 | 53 | 2014 |

ABC random forests for Bayesian parameter inference L Raynal, JM Marin, P Pudlo, M Ribatet, CP Robert, A Estoup Bioinformatics 35 (10), 1720-1728, 2018 | 41* | 2018 |

Estimation of density level sets with a given probability content B Cadre, B Pelletier, P Pudlo Journal of Nonparametric Statistics 25 (1), 261-272, 2013 | 28* | 2013 |

The normalized graph cut and Cheeger constant: from discrete to continuous E Arias-Castro, B Pelletier, P Pudlo Advances in Applied Probability 44 (4), 907-937, 2012 | 26 | 2012 |

Consistency of the adaptive multiple importance sampling JM Marin, P Pudlo, M Sedki arXiv preprint arXiv:1211.2548, 2012 | 25 | 2012 |

Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields J Stoehr, P Pudlo, L Cucala Statistics and Computing 25 (1), 129-141, 2015 | 17* | 2015 |

Operator norm convergence of spectral clustering on level sets B Pelletier, P Pudlo Journal of Machine Learning Research 12 (Feb), 385-416, 2011 | 15* | 2011 |

Efficient learning in ABC algorithms M Sedki, P Pudlo, JM Marin, CP Robert, JM Cornuet arXiv preprint arXiv:1210.1388, 2012 | 14 | 2012 |

Bayesian functional linear regression with sparse step functions PM Grollemund, C Abraham, M Baragatti, P Pudlo Bayesian Analysis 14 (1), 111-135, 2019 | 12 | 2019 |

An overview on approximate Bayesian computation M Baragatti, P Pudlo ESAIM: Proceedings 44, 291-299, 2014 | 11 | 2014 |

Contribution to the discussion of Fearnhead and Prangle (2012). Constructing summary statistics for approximate Bayesian computation: Semi-automatic approximate Bayesian … MA Sedki, P Pudlo Journal of the Royal Statistical Society: Series B 74, 466-467, 2012 | 8* | 2012 |

Likelihood-free model choice JM Marin, P Pudlo, A Estoup, C Robert Handbook of Approximate Bayesian Computation, 153-178, 2018 | 6 | 2018 |