Today my friend and coauthor Judith Rousseau is delivering a Bayesian lecture at the ICM in Philadelphia. Congrats!
Archive for Judith Rousseau
Judith Rousseau’s talk at the International Congress of Mathematicians
Posted in Statistics, University life with tags Bayesian inference, conference, Hawkes processes, ICM 2026, International Congress of Mathematicians, Judith Rousseau, Philadelphia, Université Paris Dauphine on July 25, 2026 by xi'anMarseille mais non Marseille!
Posted in Kids, Mountains, pictures, Statistics, Travel, University life with tags conference, Judith Rousseau, Lucien Le Cam, Marseiile, Mediterranean Sea, Mont Puget, Pierre Simon de Laplace, Saint-Charles, SFDS, Université Aix Marseille, Vieux-Port on June 2, 2025 by xi'an
Masterclass in Bayesian Asymptotics, Université Paris Dauphine, 18-22 March 2024
Posted in Books, pictures, Statistics, Travel, University life with tags 2024, Bayesian asymptotics, Bayesian foundations, Bayesian inference, Bernstein-von Mises theorem, bois de Boulogne, course, empirical Bayes methods, foundation lectures, France, graduate course, IMS Lecture Notes, Judith Rousseau, marginal likelihood, MASH, Master program, masterclass, Paris, PariSanté campus, Université Paris Dauphine, University of Oxford on December 8, 2023 by xi'an
On the week of 18-22 March 2024, Judith Rousseau (Paris Dauphine & Oxford) will teach a Masterclass on Bayesian asymptotics. The masterclass takes place in Paris (on the PariSanté Campus) and consists of morning lectures and afternoon labs. Attendance is free with compulsory registration before 11 March (since the building is not accessible without prior registration).
The plan of the course is as follows
Part I: Parametric models
In this part, well- and mis-specified models will be considered.
– Asymptotic posterior distribution: asymptotic normality of the posterior, penalization induced by the prior and the Bernstein von – Mises theorem. Regular and nonregular models will be treated.
– marginal likelihood and consistency of Bayes factors/model selection approaches.
– Empirical Bayes methods: asymptotic posterior distribution for parametric empirical Bayes methods.
Part II: Nonparametric and semiparametric models
– Posterior consistency and posterior convergence rates: statistical loss functions using the theory initiated by L. Schwartz and developed by Ghosal and Van der Vaart, results on less standard or well behaved losses.
– semiparametric Bernstein von Mises theorems.
– nonparametric Bernstein von Mises theorems and Uncertainty quantification.
– Stepping away from pure Bayes approaches: generalized Bayes, one step posteriors and cut posteriors.
manifold learning [BNP Seminar, 11/01/23]
Posted in Books, Statistics, University life with tags Bayesian non-parametrics, BNP, density estimator, Gaussian mixture, Judith Rousseau, manifold, University of Oxford, webinar, Zoom on January 9, 2023 by xi'an
An incoming BNP webinar on Zoom by Judith Rousseau and Paul Rosa (U of Oxford), on 11 January at 1700 Greenwich time:
Bayesian nonparametric manifold learning
In high dimensions it is common to assume that the data have a lower dimensional structure. We consider two types of low dimensional structure: in the first part the data is assumed to be concentrated near an unknown low dimensional manifold, in the second case it is assumed to be possibly concentrated on an unknown manifold. In both cases neither the manifold nor the density is known. Atypical example is for noisy observations on an unknown low dimensional manifold.
We first consider a family of Bayesian nonparametric density estimators based on location – scale Gaussian mixture priors and we study the asymptotic properties of the posterior distribution. Our work shows in particular that non conjuguate location-scale Gaussian mixture models can adapt to complex geometries and spatially varying regularity when the density is supported near a low dimensional manifold.
In the second part of the talk we will consider also the case where the distribution is supported on a low dimensional manifold. In this non dominated model,we study different types of posterior contraction rates: Wasserstein and
where
is the Haussdorff measure on the manifold
supporting the density. Some more generic results on Wasserstein contraction rates are also discussed.
Judith’s colloquium at Warwick
Posted in Statistics with tags Bayesian inference, Bayesian nonparametrics, Bayesian tests of hypotheses, colloquium, Hawkes processes, Judith Rousseau, seminar, University of Oxford, University of Warwick on February 21, 2020 by xi'an
