On the Well-posedness of Bayesian Inverse Problems-Reference-Cited by-同舟云学术

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On the Well-posedness of Bayesian Inverse Problems

Published:2020-01 Issue:1 Volume:8 Page:451-482
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Latz Jonas^ORCID

Funder

Deutsche Forschungsgemeinschaft

Technische Universität München

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Link

https://epubs.siam.org/doi/pdf/10.1137/19M1247176

Reference32 articles.

1. Bayesian posterior contraction rates for linear severely ill-posed inverse problems

2. Uncertainty Quantification in Graph-Based Classification of High Dimensional Data

3. Bayesian Probabilistic Numerical Methods

4. Uncertainty Quantification and Weak Approximation of an Elliptic Inverse Problem

Cited by 23 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Learning from small data sets: Patch‐based regularizers in inverse problems for image reconstruction;GAMM-Mitteilungen;2024-08-07

2. Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models;Advances in Computational Mathematics;2024-07-03

3. NF-ULA: Normalizing Flow-Based Unadjusted Langevin Algorithm for Imaging Inverse Problems;SIAM Journal on Imaging Sciences;2024-04-08

4. Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement;SIAM/ASA Journal on Uncertainty Quantification;2024-02-02

5. Introduction to Gaussian Process Regression in Bayesian Inverse Problems, with New Results on Experimental Design for Weighted Error Measures;Springer Proceedings in Mathematics & Statistics;2024

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