22 September 1998 Prior modeling and posterior sampling in impedance imaging
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We examine sample based Bayesian inference from impedance imaging data. We report experiments employing low level pixel based priors with mixed discrete and continuous conductivities. Sampling is carried out using Metropolis- Hasting Markov chain Monte Carlo, employing both large scale, Langevin updates, and state-adaptive local updates. Computing likelihood ratios of conductivity distributions involves solving a second order linear partial differential equation. However our simulation is rendered computationally tractable by an update procedure which employs a linearization of the forward map and thereby avoids solving the PDE for those updates which are rejected.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Geoff K. Nicholls, Geoff K. Nicholls, Colin Fox, Colin Fox, } "Prior modeling and posterior sampling in impedance imaging", Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); doi: 10.1117/12.323791; https://doi.org/10.1117/12.323791

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