10 citations · 12 across the 7 of their papers we have counts for
8 papers
Efficient Bayesian Inference in Strictly Semi-parametric Linear Inverse Problems
Adel Magra, Aad van der Vaart
We consider the efficient inference of finite dimensional parameters arising in the context of inverse problems. Our setup is the observation of a transformation of an unknown infi…
Semi-parametric Bernstein-von Mises Theorem in a Parabolic PDE Problem
Adel Magra, Frank van der Meulen, Aad van der Vaart
We consider the heat equation with absorption in a bounded domain of , where both the scalar diffusivity and the absorption function are unknown. We investigate a Bay…
Bootstrapping not under the null?
Alexis Derumigny, Miltiadis Galanis, Wieger Schipper +1
We propose a bootstrap testing framework for a general class of hypothesis tests, which allows resampling under the null hypothesis as well as other forms of bootstrapping. We iden…
Semiparametric Bernstein-von Mises Phenomenon via Isotonized Posterior in Wicksell's problem
Francesco Gili, Geurt Jongbloed, Aad van der Vaart
In this paper, we propose a novel Bayesian approach for nonparametric estimation in Wicksell's problem. This has important applications in astronomy for estimating the distribution…
The Bernstein-von Mises theorem for Semiparametric Mixtures
Stefan Franssen, Jeanne Nguyen, Aad van der Vaart
Semiparametric mixture models are parametric models with latent variables. They are defined kernel, , where z is the unknown latent variable, and is the parameter o…
Linear methods for non-linear inverse problems
Geerten Koers, Botond Szabo, Aad van der Vaart
We consider the recovery of an unknown function from a noisy observation of the solution to a partial differential equation that can be written in the form $\mathcal{L} u…
Bayesian linear inverse problems in regularity scales
Shota Gugushvili, Aad van der Vaart, Dong Yan
We obtain rates of contraction of posterior distributions in inverse problems defined by scales of smoothness classes. We derive abstract results for general priors, with contracti…
Adaptive posterior contraction rates for the horseshoe
Stéphanie van der Pas, Botond Szabó, Aad van der Vaart
We investigate the frequentist properties of Bayesian procedures for estimation based on the horseshoe prior in the sparse multivariate normal means model. Previous theoretical res…