Randomize-Then-Optimize for Sampling and Uncertainty Quantification in Electrical Impedance Tomography

DOI10.1137/140978272zbMath1381.94010OpenAlexW2195579312MaRDI QIDQ2801319

Johnathan M. Bardsley, Antti Solonen, Heikki Haario, Aku Seppänen, Jari P. Kaipio

Publication date: 6 April 2016

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/bb99a660a3fa46a80fd29ff4556a27bc2d2abdb3

zbMATH Keywords

inverse problemsMarkov chain Monte Carlonumerical optimizationelectrical impedance tomographyBayesian methods

Mathematics Subject Classification ID

Related Items (5)

Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification ⋮ Sparsity promoting reconstructions via hierarchical prior models in diffuse optical tomography ⋮ A Benchmark for the Bayesian Inversion of Coefficients in Partial Differential Equations ⋮ Relaxed Gauss--Newton Methods with Applications to Electrical Impedance Tomography ⋮ Quantification of measurement error effects on conductivity reconstruction in electrical impedance tomography

Uses Software

• BayesDA

Cites Work

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• Electrical Impedance Tomography
• Inverse problems with structural prior information
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