Dear Colleagues,

Inverse problems arise in many applications. Whatever the domain of application, when the unknown quantities on which we want to infer, the quantities on which we can do measurements, and the mathematical relations linking them have been identified, the problem then becomes inference. To this end, deterministic regularization methods have been successfully developed and used. Two main difficulties still remain: how to choose the different criteria and how to weight them and quantify their uncertainties. In the three last decades, the probabilistic methods and, in particular, the Bayesian approach have shown their efficiency. The focus of this Special Issue is to present original papers on such probabilistic methods where the real advantages on regularization methods have been demonstrated. Papers with real applications in different areas such as biological and medical imaging, industrial nondestructive testing, radio astronomical, and geophysical imaging are preferred.

Prof. Dr. Ali Mohammad-Djafari
Guest Editor

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• inverse problems
• Bayesian inference
• approximate Bayesian computation
• variational methods
• variational Bayesian approximation
• expectation propagation
• uncertainty quantification
• MCMC
• computed tomography
• medical imaging
• nondestructive industrial imaging