Posterior Contraction in Bayesian Inverse Problems Under Gaussian Priors

DOI10.1007/978-3-319-70824-9_1zbMath1405.62051OpenAlexW2791345636MaRDI QIDQ4554170

Peter Mathé, Sergios Agapiou

Publication date: 13 November 2018

Published in: Trends in Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-70824-9_1

zbMATH Keywords

Gaussian noiseBayesian inference

Mathematics Subject Classification ID

Bayesian inference (62F15) Nonparametric inference (62G99)

Related Items (7)

Designing truncated priors for direct and inverse Bayesian problems ⋮ Bayesian inverse problems with heterogeneous variance ⋮ Hyperparameter estimation in Bayesian MAP estimation: parameterizations and consistency ⋮ Are Minimizers of the Onsager–Machlup Functional Strong Posterior Modes? ⋮ Convergence Rates for Learning Linear Operators from Noisy Data ⋮ Posterior contraction for empirical Bayesian approach to inverse problems under non-diagonal assumption ⋮ Bayesian inverse problems with non-commuting operators

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