Abstract
In the Bayesian approach to ill-posed inverse problems, regularization is imposed by specifying a prior distribution on the parameters of interest and Markov chain Monte Carlo samplers are used to extract information about its posterior distribution. The aim of this paper is to investigate the convergence properties of the random-scan random-walk Metropolis (RSM) algorithm for posterior distributions in ill-posed inverse problems. We provide an accessible set of sufficient conditions, in terms of the observational model and the prior, to ensure geometric ergodicity of RSM samplers of the posterior distribution. We illustrate how these conditions can be checked in an application to the inversion of oceanographic tracer data. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.
| Original language | English |
|---|---|
| Pages (from-to) | 839-853 |
| Journal | Scandinavian Journal of Statistics |
| Volume | 36 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Dec 2009 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- Advection-diffusion
- Bayesian regularization
- Geometric ergodicity
- Markov chain Monte Carlo
- Ocean circulation
- Random-scan Metropolis
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