A general approach to posterior contraction in nonparametric inverse problems
From MaRDI portal
Publication:1697047
DOI10.3150/16-BEJ921zbMath1419.62089arXiv1407.0335OpenAlexW2963656508MaRDI QIDQ1697047
Bartek Knapik, Jean-Bernard Salomond
Publication date: 15 February 2018
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1407.0335
modulus of continuitywhite noiseBayesian nonparametricsposterior distributionnonparametric regressionnonparametric inverse problemsrate of contractionminimax adaptive contraction rate
Nonparametric regression and quantile regression (62G08) Minimax procedures in statistical decision theory (62C20)
Related Items (14)
Designing truncated priors for direct and inverse Bayesian problems ⋮ Consistency of Bayesian inference with Gaussian process priors for a parabolic inverse problem ⋮ Posterior Contraction in Bayesian Inverse Problems Under Gaussian Priors ⋮ Bayesian inverse problems with heterogeneous variance ⋮ Convergence Rates for Learning Linear Operators from Noisy Data ⋮ Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions ⋮ Bayesian linear inverse problems in regularity scales ⋮ Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems ⋮ Posterior contraction for empirical Bayesian approach to inverse problems under non-diagonal assumption ⋮ Bernstein--von Mises Theorems and Uncertainty Quantification for Linear Inverse Problems ⋮ Posterior convergence for Bayesian functional linear regression ⋮ Large deviations for infinite weighted sums of stretched exponential random variables ⋮ Bernstein-von Mises theorems for statistical inverse problems. I: Schrödinger equation ⋮ Posterior contraction rates for deconvolution of Dirichlet-Laplace mixtures
This page was built for publication: A general approach to posterior contraction in nonparametric inverse problems
