We provide the rest of the proofs of the results contained in the main article, some technical
lemmas as well as additional simulations corroborating the theory. We also provide a …

arXiv:2402.16422v1 [math.ST] 26 Feb 2024 Page 1 Bay!ian nonparamet"c #at$tics St-Flour
lecture notes Ismaël Castillo arXiv:2402.16422v1 [math.ST] 26 Feb 2024 Page 2 2 Principe. Si …

The supplement [19] contains additional material, including results for nonparametric
regression, a simulation study, an adaptive nonparametric Bernstein–von Mises theorem …

Bayesian methods are a prominent tool in statistics, machine learning, and practical
applications of statistics. Let us give a few examples from different application fields. 1 …

This chapter considers more flexible choices of prior distributions that allow, at least for
certain loss functions, to automatically attain minimax optimal posterior convergence rates …

This chapter continues the study of Bernstein–von Mises (BvM) theorems. Here we consider
the question of estimating an infinite-dimensional object, that is, a nonparametric framework …

Modern data analysis provides scientists with statistical and machine learning algorithms
with impressive performance. In front of their extensive use to tackle problems of constantly …

• In Section S3, we prove the posterior concentration on small Hellinger neighborhoods of
the true density. This is the first step toward concentration in stronger distances.• In Section …