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Submitted to the "International Conference On Electrical Bio-Impedance V: Electrical Impedance Tomography", Gdansk, Poland, 2004
 

Summary: Submitted to the "International Conference On Electrical Bio-Impedance V: Electrical Impedance Tomography",
Gdansk, Poland, 2004
BAYES-MCMC RECONSTRUCTION FROM 3D EIT DATA
USING A COMBINED LINEAR AND NON-LINEAR FORWARD
PROBLEM SOLUTION STRATEGY
M Soleimani1a
, RG Aykroyd2
, RM West3
, S Meng2
, WRB Lionheart1
and N Polydorides1
1
Department of Mathematic, UMIST, Manchester, M60 1QD, UK
2
Department of Statistics, University of Leeds, Leeds, LS2 9JT, UK
3
Biostatistics Unit, University of Leeds, Leeds, LS2 9JT, UK
ABSTRACT: Extracting meaningful information from EIT data is a challenging task due to highly correlated data
and substantial noise. Domain discretisation further complicates the situation by also making it an ill-posed problem
requiring substantial regularisation. The Bayesian approach not only provides a natural setting in which to specify

  

Source: Aykroyd, Robert G. - Department of Statistics, University of Leeds

 

Collections: Mathematics