Julyan Arbel and Jean-Bernard Salomond. Sequential quasi monte carlo for dirichlet process mixture models. In NIPS-Conference on Neural Informa- tion Processing Systems, 2016. URL https://hal.archives-ouvertes.fr/hal-01405568.
Bartek Knapik and Jean-Bernard Salomond. A general approach to posterior contraction in nonparametric inverse problems. Bernoulli, 24(3):2091–2121, 2018. URL https://arxiv.org/abs/1407.0335.
Judith Rousseau, Jean-Bernard Salomond, and Catia Scricciolo. On some as- pects of the asymptotic properties of bayesian approaches in nonparametric and semiparametric models. In ESAIM: Proceedings, volume 44, pages 159– 171. EDP Sciences, 2014. URL https://www.esaim-proc.org/articles/proc/pdf/2014/01/proc144410.pdf
Jean-Bernard Salomond. Concentration rate and consistency of the posterior distribution for selected priors under monotonicity constraints. Electronic journal of statistics, 8(1):1380–1404, 2014b. URL http://projecteuclid.org/download/pdfview_1/euclid.ejs/1408540291.
Jean-Bernard Salomond. Propriétés fréquentistes des méthodes Bayésiennes semi-paramétriques et non paramétriques. PhD thesis, Université Paris Dauphine-Paris IX, 2014c. URL Link.
Jean-Bernard Salomond. Risk quantification for the thresholding rule for multiple testing using gaussian scale mixtures. arXiv preprint arXiv:1711.08705, 2017. URL https://arxiv.org/pdf/1711.08705.pdf.
SL van der Pas, J-B Salomond, Johannes Schmidt-Hieber, et al. Conditions for posterior contraction in the sparse normal means problem. Electronic journal of statistics, 10(1):976–1000, 2016. URL http://projecteuclid.org/euclid.ejs/1460463652.