Judith Rousseau
Judith Rousseau
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Cited by
Cited by
Redefine statistical significance
DJ Benjamin, JO Berger, M Johannesson, BA Nosek, EJ Wagenmakers, ...
Nature human behaviour 2 (1), 6-10, 2018
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
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
Harold Jeffreys’s theory of probability revisited
CP Robert, N Chopin, J Rousseau
Statistical Science 24 (2), 141-172, 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
Adaptive Bayesian density estimation with location-scale mixtures
W Kruijer, J Rousseau, A Van Der Vaart
Electronic journal of statistics 4, 1225-1257, 2010
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
Combining expert opinions in prior elicitation
I Albert, S Donnet, C Guihenneuc-Jouyaux, S Low-Choy, K Mengersen, ...
Bayesian Analysis 7 (3), 503-532, 2012
Bernstein–von Mises theorem for linear functionals of the density
V Rivoirard, J Rousseau
The Annals of Statistics 40 (3), 1489-1523, 2012
A Bernstein–von Mises theorem for smooth functionals in semiparametric models
I Castillo, J Rousseau
The Annals of Statistics 43 (6), 2353-2383, 2015
Asymptotic properties of approximate Bayesian computation
DT Frazier, GM Martin, CP Robert, J Rousseau
Biometrika 105 (3), 593-607, 2018
On adaptive posterior concentration rates
M Hoffmann, J Rousseau, J Schmidt-Hieber
The Annals of Statistics 43 (5), 2259-2295, 2015
Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparametric estimation of the density
J Rousseau
The Annals of Statistics 38 (1), 146-180, 2010
Testing hypotheses via a mixture estimation model
K Kamary, K Mengersen, CP Robert, J Rousseau
arXiv preprint arXiv:1412.2044, 2014
On the selection of initialization and activation function for deep neural networks
S Hayou, A Doucet, J Rousseau
arXiv preprint arXiv:1805.08266, 2018
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
Bayes and empirical Bayes: do they merge?
S Petrone, J Rousseau, C Scricciolo
Biometrika 101 (2), 285-302, 2014
Bayesian optimal adaptive estimation using a sieve prior
J Arbel, G Gayraud, J Rousseau
Scandinavian journal of statistics 40 (3), 549-570, 2013
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
Posterior concentration rates for infinite dimensional exponential families
V Rivoirard, J Rousseau
Bayesian Analysis 7 (2), 311-334, 2012
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