Implicitly Localized MCMC Sampler to Cope With Non-local/Non-linear Data Constraints in Large-Size Inverse Problems

Implicitly Localized MCMC Sampler to Cope With Non-local/Non-linear Data Constraints in Large-Size Inverse Problems

Many practical applications involve the resolution of large-size inverse problems, without providing more than a moderate-size sample to describe the prior probability distribution. In this situation, additional information must be supplied to augment the effective dimension of the available sample....

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Bibliographic Details
Main Author: Jean-Michel Brankart
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-11-01
Series:Frontiers in Applied Mathematics and Statistics
Subjects:
inverse method
data assimilation
ensemble
non-linear constraints
localization
MCMC algorithm
Online Access:https://www.frontiersin.org/article/10.3389/fams.2019.00058/full

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https://www.frontiersin.org/article/10.3389/fams.2019.00058/full

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