v1v2 (latest)

Statistical guarantees for Bayesian uncertainty quantification in non-linear inverse problems with Gaussian process priors

31 July 2020

Papers citing "Statistical guarantees for Bayesian uncertainty quantification in non-linear inverse problems with Gaussian process priors"

21 / 21 papers shown

Title
Valid Credible Ellipsoids for Linear Functionals by a Renormalized Bernstein-von Mises Theorem Gustav Rømer 35 0 0 17 Sep 2024
Convergence Rates for the Maximum A Posteriori Estimator in PDE-Regression Models with Random Design Maximilian Siebel 58 1 0 05 Sep 2024
On the Frequentist Coverage of Bayes Posteriors in Nonlinear Inverse Problems You-Hyun Baek Katerina Papagiannouli Sayan Mukherjee 59 0 0 19 Jul 2024
On the estimation rate of Bayesian PINN for inverse problems Yi Sun Debarghya Mukherjee Yves Atchadé PINN 110 1 0 21 Jun 2024
Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems Lorenzo Baldassari Ali Siahkoohi Josselin Garnier K. Sølna Maarten V. de Hoop DiffM 129 2 0 24 May 2024
Deep Gaussian Process Priors for Bayesian Inference in Nonlinear Inverse Problems Kweku Abraham Neil Deo 41 5 0 21 Dec 2023
Stability and Statistical Inversion of Travel time Tomography Ashwin Tarikere Hanming Zhou 36 1 0 22 Sep 2023
Misspecified Bernstein-Von Mises theorem for hierarchical models Geerten Koers Botond Szabó A. van der Vaart 70 2 0 15 Aug 2023
On posterior consistency of data assimilation with Gaussian process priors: the 2D Navier-Stokes equations Richard Nickl E. Titi 49 8 0 16 Jul 2023
Consistent inference for diffusions from low frequency measurements Richard Nickl 59 8 0 24 Oct 2022
On free energy barriers in Gaussian priors and failure of cold start MCMC for high-dimensional unimodal distributions Afonso S. Bandeira Antoine Maillard Richard Nickl Sven Wang 83 10 0 05 Sep 2022
A Bernstein--von-Mises theorem for the Calderón problem with piecewise constant conductivities Jan Bohr 57 2 0 16 Jun 2022
Uncertainty Quantification for nonparametric regression using Empirical Bayesian neural networks Stefan Franssen Botond Szabó BDL UQCV 89 4 0 27 Apr 2022
Gaussian Process Regression in the Flat Limit Simon Barthelmé P. Amblard Nicolas M Tremblay K. Usevich GP 36 5 0 04 Jan 2022
Minimax detection of localized signals in statistical inverse problems Markus Pohlmann Frank Werner Axel Munk 45 1 0 10 Dec 2021
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices Jan Povala Ieva Kazlauskaite Eky Febrianto F. Cirak Mark Girolami 91 23 0 22 Oct 2021
Uncertainty quantification in the Bradley-Terry-Luce model Chao Gao Yandi Shen A. Zhang 93 22 0 08 Oct 2021
On some information-theoretic aspects of non-linear statistical inverse problems Richard Nickl G. Paternain 79 9 0 20 Jul 2021
On log-concave approximations of high-dimensional posterior measures and stability properties in non-linear inverse problems Jan Bohr Richard Nickl 57 17 0 17 May 2021
Consistency of Bayesian inference with Gaussian process priors for a parabolic inverse problem Hanne Kekkonen 52 12 0 24 Mar 2021
Deep learning architectures for nonlinear operator functions and nonlinear inverse problems Maarten V. de Hoop Matti Lassas C. Wong 78 26 0 23 Dec 2019