Bayesian inverse problems with non-commuting operators

DOI10.1090/mcom/3439zbMath1421.62028arXiv1801.09540OpenAlexW2785748791WikidataQ128232580 ScholiaQ128232580MaRDI QIDQ5226663

Peter Mathé

Publication date: 1 August 2019

Published in: Mathematics of Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1801.09540

zbMATH Keywords

Bayesian inverse problemsill-posed operator equations

Mathematics Subject Classification ID

Bayesian inference (62F15) Linear operator methods in interpolation, moment and extension problems (47A57) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items

Designing truncated priors for direct and inverse Bayesian problems, Convergence analysis of Tikhonov regularization for non-linear statistical inverse problems, Inverse learning in Hilbert scales, On the Asymptotical Regularization for Linear Inverse Problems in Presence of White Noise, Posterior contraction for empirical Bayesian approach to inverse problems under non-diagonal assumption

Cites Work

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• Oracle-type posterior contraction rates in Bayesian inverse problems
• Bayesian inverse problems with Gaussian priors
• Regularisierung schlecht gestellter Probleme durch Projektionsverfahren
• Posterior contraction rates for the Bayesian approach to linear ill-posed inverse problems
• Discrepancy principle for statistical inverse problems with application to conjugate gradient iteration
• Interpolation in variable Hilbert scales with application to inverse problems
• The finite element method for ill-posed problems
• Posterior Contraction in Bayesian Inverse Problems Under Gaussian Priors
• Bayesian Recovery of the Initial Condition for the Heat Equation
• Convergence rates for Tikhonov regularization from different kinds of smoothness conditions
• On Majorization, Factorization, and Range Inclusion of Operators on Hilbert Space