Chapter 18: Computational methods for nonlinear Bayesian inverse problems
Abstract
We focus on the problem setup outlined in Chapter 15. Considering a discretized version of (15.1), we focus on estimation of \(m \in \mathbb{R}^N_{\text M}\) using the model\[y = f(m) + \eta.\](18.1)Here, \(m\) is a discretized inversion parameter, \(f\) is a discretized nonlinear parameter-to-observable mapping, and \(y\) is a vector of measurement data.
Fields of Study & Subjects
Markov Chains and Monte Carlo Methods
Numerical methods in inverse problems
Gaussian Processes and Bayesian Inference
Publication details
- Journal / source
- Society for Industrial and Applied Mathematics eBooks
- Pages
- 255–262
- Publication date
- 27 February 2026
- Language
- EN
- Document type
- Book chapter