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Judith Rousseau
Judith Rousseau
Verified email at stats.ox.ac.uk - Homepage
Title
Cited by
Cited by
Year
Redefine statistical significance
DJ Benjamin, JO Berger, M Johannesson, BA Nosek, EJ Wagenmakers, ...
Nature human behaviour 2 (1), 6-10, 2018
27532018
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
3622011
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
3282004
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
2402019
Harold Jeffreys’s theory of probability revisited
CP Robert, N Chopin, J Rousseau
2142009
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
1632014
Adaptive Bayesian density estimation with location-scale mixtures
W Kruijer, J Rousseau, A Van Der Vaart
1572010
A Bayesian information criterion for singular models
M Drton, M Plummer
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017
1472017
Combining expert opinions in prior elicitation
I Albert, S Donnet, C Guihenneuc-Jouyaux, S Low-Choy, K Mengersen, ...
1422012
A Bernstein–von Mises theorem for smooth functionals in semiparametric models
I Castillo, J Rousseau
1322015
Bernstein–von Mises theorem for linear functionals of the density
V Rivoirard, J Rousseau
1222012
Asymptotic properties of approximate Bayesian computation
DT Frazier, GM Martin, CP Robert, J Rousseau
Biometrika 105 (3), 593-607, 2018
1162018
Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparametric estimation of the density
J Rousseau
922010
On the selection of initialization and activation function for deep neural networks
S Hayou, A Doucet, J Rousseau
arXiv preprint arXiv:1805.08266, 2018
882018
On adaptive posterior concentration rates
M Hoffmann, J Rousseau, J Schmidt-Hieber
872015
Testing hypotheses via a mixture estimation model
K Kamary, K Mengersen, CP Robert, J Rousseau
arXiv preprint arXiv:1412.2044, 2014
852014
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
762008
Bayes and empirical Bayes: do they merge?
S Petrone, J Rousseau, C Scricciolo
Biometrika 101 (2), 285-302, 2014
742014
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
692015
Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator
J Rousseau, B Szabo
682017
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Articles 1–20