Information field theory (IFT) is a Bayesian statistical field theory relating to signal reconstruction, cosmography, and other related areas.[1][2] IFT summarizes the information available on a physical field using Bayesian probabilities. It uses computational techniques developed for quantum field theory and statistical field theory to handle the infinite number of degrees of freedom of a field and to derive algorithms for the calculation of field expectation values. For example, the posterior expectation value of a field generated by a known Gaussian process and measured by a linear device with known Gaussian noise statistics is given by a generalized Wiener filter applied to the measured data. IFT extends such known filter formula to situations with nonlinear physics, nonlinear devices, non-Gaussian field or noise statistics, dependence of the noise statistics on the field values, and partly unknown parameters of measurement. For this it uses Feynman diagrams, renormalisation flow equations, and other methods from mathematical physics.[3]
^Enßlin, Torsten A. (2019). "Information theory for fields". Annalen der Physik. 531 (3): 1800127. arXiv:1804.03350. Bibcode:2019AnP...53100127E. doi:10.1002/andp.201800127.
^"Information field theory". Max Planck Society. Retrieved 13 Nov 2014.
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