Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction-Reference-Cited by-同舟云学术

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

Published:2016-06 Issue: Volume:315 Page:363-387
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Cui Tiangang,Marzouk Youssef,Willcox Karen

Funder

Department of Energy

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modeling and Simulation,Numerical Analysis

Reference60 articles.

1. Inverse Problem Theory and Methods for Model Parameter Estimation;Tarantola,2005

2. Statistical and Computational Inverse Problems;Kaipio,2004

3. Inverse problems: a Bayesian perspective;Stuart;Acta Numer.,2010

4. Markov Chain Monte Carlo in Practice, vol. 2,1996

5. Monte Carlo Strategies in Scientific Computing;Liu,2001

Cited by 41 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 to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides;Journal of Computational Physics;2024-08

2. A MCMC method based on surrogate model and Gaussian process parameterization for infinite Bayesian PDE inversion;Journal of Computational Physics;2024-06

3. Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems;Inverse Problems;2024-02-05

4. A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models;Journal of Computational Physics;2024-02

5. Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events;SIAM Journal on Scientific Computing;2024-01-24