Local Sensitivity Analysis for Bayesian Inverse Problems
Abstract
Abstract. We present an extension of local sensitivity analysis, also referred to as the perturbation approach for uncertainty quantification, to Bayesian inverse problems. More precisely, we show how moments of random variables with respect to the posterior distribution can be approximated efficiently by asymptotic expansions. This is under the assumption that the measurement operators and prediction functions are sufficiently smooth and that their corresponding stochastic moments with respect to the prior distribution exist. Numerical experiments are presented to the illustrate the theoretical results.
Fields of Study & Subjects
Probabilistic and Robust Engineering Design
Markov Chains and Monte Carlo Methods
Gaussian Processes and Bayesian Inference
Publication details
- Journal / source
- SIAM/ASA Journal on Uncertainty Quantification
- Volume
- 14
- Issue
- 2
- Pages
- 341–368
- Publication date
- 2 April 2026
- Language
- EN