| Redefine statistical significance DJ Benjamin, JO Berger, M Johannesson, BA Nosek, EJ Wagenmakers, ... Nature human behaviour 2 (1), 6-10, 2018 | 2775 | 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 | 364 | 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 | 326 | 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 | 243 | 2019 |
| Harold Jeffreys's theory of probability revisited CP Robert, N Chopin, J Rousseau Statistical Science, 141-172, 2009 | 215 | 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 | 161 | 2014 |
| Adaptive Bayesian density estimation with location-scale mixtures W Kruijer, J Rousseau, A Van Der Vaart | 159 | 2010 |
| A Bayesian information criterion for singular models M Drton, M Plummer Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017 | 150 | 2017 |
| Combining expert opinions in prior elicitation I Albert, S Donnet, C Guihenneuc-Jouyaux, S Low-Choy, K Mengersen, ... | 144 | 2012 |
| A Bernstein–von Mises theorem for smooth functionals in semiparametric models I Castillo, J Rousseau | 131 | 2015 |
| Bernstein–von Mises theorem for linear functionals of the density V Rivoirard, J Rousseau | 122 | 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 | 93 | 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 | 90 | 2018 |
| On adaptive posterior concentration rates M Hoffmann, J Rousseau, J Schmidt-Hieber | 90 | 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 | 73 | 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 | 70 | 2020 |
| Overfitting Bayesian mixture models with an unknown number of components Z Van Havre, N White, J Rousseau, K Mengersen PloS one 10 (7), e0131739, 2015 | 69 | 2015 |
