Inference with minimal Gibbs free energy in information field theory

@article{Ensslin2010InferenceWM,
  title={Inference with minimal Gibbs free energy in information field theory},
  author={Torsten A. Ensslin and Cornelius Weig},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  year={2010},
  volume={82 5 Pt 1},
  pages={
          051112
        }
}
  • T. EnsslinCornelius Weig
  • Published 16 April 2010
  • Computer Science
  • Physical review. E, Statistical, nonlinear, and soft matter physics
Non-linear and non-gaussian signal inference problems are difficult to tackle. Renormalization techniques permit us to construct good estimators for the posterior signal mean within information field theory (IFT), but the approximations and assumptions made are not very obvious. Here we introduce the simple concept of minimal Gibbs free energy to IFT, and show that previous renormalization results emerge naturally. They can be understood as being the gaussian approximation to the full posterior… 

Comment on "Inference with minimal Gibbs free energy in information field theory".

It is shown explicitly that MGFE indeed incorporates the MAP principle, as well as the MDI (minimum discrimination information) approach, but not the well-known ME principle of Jaynes, which is important for Bayesian inference problems.

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