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Published November 2016 | Submitted
Journal Article Open

The Bayesian formulation of EIT: Analysis and algorithms

Abstract

We provide a rigorous Bayesian formulation of the EIT problem in an infinite dimensional setting, leading to well-posedness in the Hellinger metric with respect to the data. We focus particularly on the reconstruction of binary fields where the interface between different media is the primary unknown. We consider three different prior models -log-Gaussian, star-shaped and level set. Numerical simulations based on the implementation of MCMC are performed, illustrating the advantages and disadvantages of each type of prior in the reconstruction, in the case where the true conductivity is a binary field, and exhibiting the properties of the resulting posterior distribution.

Additional Information

© 2016 American Institute of Mathematical Sciences Received: August 2015; Revised: July 2016; Available Online: October 2016. MMD is supported by EPSRC grant EP/HO23364/1 as part of the MASDOC DTC at the University of Warwick. AMS is supported by EPSRC and ONR. This research utilised Queen Mary's MidPlus computational facilities, supported by QMUL Research-IT and funded by EPSRC grant EP/K000128/1.

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August 20, 2023
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