41 citations · 67 across the 9 of their papers we have counts for
4 papers · 1 filter
Designing truncated priors for direct and inverse Bayesian problems
Sergios Agapiou, Peter Mathé
The Bayesian approach to inverse problems with functional unknowns, has received significant attention in recent years. An important component of the developing theory is the study…
Inverse learning in Hilbert scales
Abhishake Rastogi, Peter Mathé
We study the linear ill-posed inverse problem with noisy data in the statistical learning setting. Approximate reconstructions from random noisy data are sought with general regula…
Lepskii Principle in Supervised Learning
Gilles Blanchard, Peter Mathé, Nicole Mücke
In the setting of supervised learning using reproducing kernel methods, we propose a data-dependent regularization parameter selection rule that is adaptive to the unknown regulari…
Preconditioning the prior to overcome saturation in Bayesian inverse problems
Sergios Agapiou, Peter Mathé
We study Bayesian inference in statistical linear inverse problems with Gaussian noise and priors in Hilbert space. We focus our interest on the posterior contraction rate in the s…