Skip to Main content Skip to Navigation
Journal articles

Bayesian inversion by parallel interacting Markov chains

Abstract : Markov chains Monte-Carlo (MCMC) methods are known to produce samples of virtually any distribution. They have already been widely used in the resolution of non-linear inverse problems where no analytical expression for the forward relation between data and model parameters is available, and where linearization is unsuccessful. However, in Bayesian inversion, the total number of simulations we can afford is highly related to the computational cost of the forward model. Hence, the complete browsing of the support of the posterior distribution is hardly performed at final time, especially when the posterior is high dimensional and/or multimodal. In the latter case, the chain may stay stuck in one of the modes. Recently, the idea of making interact several Markov chains at different temperatures has been explored. These methods improve the mixing properties of classical single MCMC. Furthermore, these methods can make efficient use of large CPU clusters, without increasing the global computational cost with respect to classical MCMC.
Complete list of metadatas

https://hal-mines-paristech.archives-ouvertes.fr/hal-00505406
Contributor : Pascale Nalon <>
Submitted on : Friday, July 23, 2010 - 3:39:21 PM
Last modification on : Wednesday, October 14, 2020 - 4:09:54 AM
Long-term archiving on: : Monday, October 25, 2010 - 12:15:19 PM

File

article_inverse-romary-2009.pd...
Files produced by the author(s)

Identifiers

Citation

Thomas Romary. Bayesian inversion by parallel interacting Markov chains. Inverse Problems in Science and Engineering, Taylor & Francis, 2010, 18 (1), pp.111-130. ⟨10.1080/17415970903234620⟩. ⟨hal-00505406⟩

Share

Metrics

Record views

452

Files downloads

381