ANOVA-GP Modeling for High-Dimensional Bayesian Inverse Problems

ANOVA-GP Modeling for High-Dimensional Bayesian Inverse Problems

Markov chain Monte Carlo (MCMC) stands out as an effective method for tackling Bayesian inverse problems. However, when dealing with computationally expensive forward models and high-dimensional parameter spaces, the challenge of repeated sampling becomes pronounced. A common strategy to address thi...

Full description

Bibliographic Details
Main Authors: Xiaoyu Shi, Hanyu Zhang, Guanjie Wang
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Mathematics
Subjects:
Bayesian inverse problem
uncertainty quantification
ANOVA decomposition
principle component analysis
Gaussian process regression
Online Access:https://www.mdpi.com/2227-7390/12/2/301

Internet

https://www.mdpi.com/2227-7390/12/2/301

Similar Items