Redefine statistical significance DJ Benjamin, JO Berger, M Johannesson, BA Nosek, EJ Wagenmakers, ... Nature human behaviour 2 (1), 6-10, 2018 | 2757 | 2018 |

Asymptotic behaviour of the posterior distribution in overfitted mixture models J Rousseau, K Mengersen Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2011 | 362 | 2011 |

Optimal sample size for multiple testing: the case of gene expression microarrays P Müller, G Parmigiani, C Robert, J Rousseau Journal of the American Statistical Association 99 (468), 990-1001, 2004 | 329 | 2004 |

On the impact of the activation function on deep neural networks training S Hayou, A Doucet, J Rousseau International conference on machine learning, 2672-2680, 2019 | 242 | 2019 |

Harold Jeffreys’s theory of probability revisited CP Robert, N Chopin, J Rousseau | 216 | 2009 |

Relevant statistics for Bayesian model choice JM Marin, NS Pillai, CP Robert, J Rousseau Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2014 | 163 | 2014 |

Adaptive Bayesian density estimation with location-scale mixtures W Kruijer, J Rousseau, A Van Der Vaart | 157 | 2010 |

A Bayesian information criterion for singular models M Drton, M Plummer Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017 | 147 | 2017 |

Combining expert opinions in prior elicitation I Albert, S Donnet, C Guihenneuc-Jouyaux, S Low-Choy, K Mengersen, ... | 143 | 2012 |

A Bernstein–von Mises theorem for smooth functionals in semiparametric models I Castillo, J Rousseau | 133 | 2015 |

Bernstein–von Mises theorem for linear functionals of the density V Rivoirard, J Rousseau | 123 | 2012 |

Asymptotic properties of approximate Bayesian computation DT Frazier, GM Martin, CP Robert, J Rousseau Biometrika 105 (3), 593-607, 2018 | 119 | 2018 |

Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparametric estimation of the density J Rousseau | 92 | 2010 |

On the selection of initialization and activation function for deep neural networks S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1805.08266, 2018 | 88 | 2018 |

On adaptive posterior concentration rates M Hoffmann, J Rousseau, J Schmidt-Hieber | 87 | 2015 |

Testing hypotheses via a mixture estimation model K Kamary, K Mengersen, CP Robert, J Rousseau arXiv preprint arXiv:1412.2044, 2014 | 85 | 2014 |

Quantitative risk assessment from farm to fork and beyond: A global Bayesian approach concerning food‐borne diseases I Albert, E Grenier, JB Denis, J Rousseau Risk Analysis: An International Journal 28 (2), 557-571, 2008 | 76 | 2008 |

Bayes and empirical Bayes: do they merge? S Petrone, J Rousseau, C Scricciolo Biometrika 101 (2), 285-302, 2014 | 74 | 2014 |

Model misspecification in approximate Bayesian computation: consequences and diagnostics DT Frazier, CP Robert, J Rousseau Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2020 | 69 | 2020 |

Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator J Rousseau, B Szabo | 69 | 2017 |