The article discusses the electrical impedance imaging problem (EIT) from a Bayesian point of view. We discuss two essentially different EIT problems: The first one is the static problem of estimating the resistivity distribution of a body from the static current/voltage measurements on the surface of the body. The other problem is a gas temperature distribution retrieval problem by resistivity measurements of metal filaments placed in the gas funnel. In these examples, the prior information contains inequality constraints and non-smooth functionals. Consequently, gradient-based maximum likelihood search algorithms converge poorly. To overcome this difficulty, we study the possibility of using a Markov chain Monte Carlo algorithm to explore the posterior distribution.

Date Published: 9 December 1997
PDF: 11 pages
Proc. SPIE 3171, Computational, Experimental, and Numerical Methods for Solving Ill-Posed Inverse Imaging Problems: Medical and Nonmedical Applications, (9 December 1997); doi: 10.1117/12.279723

Published in SPIE Proceedings Vol. 3171:
Computational, Experimental, and Numerical Methods for Solving Ill-Posed Inverse Imaging Problems: Medical and Nonmedical Applications
Randall Locke Barbour; Mark J. Carvlin; Michael A. Fiddy, Editor(s)