Archive for 2024
an upside down year [cover]
Posted in Books with tags 2024, cover, Donald Trump, Emmanuel Macron, French politics, Léon Marchand, Libé, Middle East, Monique Pélicot, New Year's Eve, Paris 2024 Olympics, political crisis, rape, sexual violence, Solidarity with Ukraine, Syrian civil war, UNiTE to End Violence against Women on December 30, 2024 by xi'an
Xmastly Monte Carlo [13/12]
Posted in Statistics with tags 2024, diffusion sampler, importance sampling, Issy-les-Moulineaux, MCMC, Mostly MC, multimodal target, Ocean, Paris, PariSanté campus, PDMP, Porte de Versailles, reliability, seminar, Xmas on December 7, 2024 by xi'an
be EU!
Posted in Kids, pictures, Running, Travel, University life with tags 2024, Belgium, Belgium Parliament, Brussels, EU, European elections, European Parliament, jatp, more Europe, Use Your Vote on June 8, 2024 by xi'an
Burning through the frozen south [and happy new year!]
Posted in Statistics with tags 2024, Antarctic Peninsula, Antarctica, climate change, Happy New Year, Michael Meredith, Royal Society, The Royal Society Photography Competition 2023 on January 1, 2024 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.
