About: Information field theory

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Information field theory (IFT) is a Bayesian statistical field theory relating to signal reconstruction, cosmography, and other related areas. 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-

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rdf:type	work
rdfs:label	Information field theory (en)
rdfs:comment	Information field theory (IFT) is a Bayesian statistical field theory relating to signal reconstruction, cosmography, and other related areas. 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- (en)
foaf:depiction
dcterms:subject	Bayesian inference Bayesian statistics
Wikipage page ID	44391005 (xsd:integer)
Wikipage revision ID	1102598925 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayesian hierarchical modeling Bayesian inference Bayesian probability Quantum field theory Cosmic Microwave Background Bayes' theorem Path integral formulation Renormalisation Degrees of freedom (statistics) Inflationary epoch Critical point (thermodynamics) Bayesian inference Gaussian noise Generalized Wiener filter Gaussian process Gibbs free energy Cosmography Empirical Bayes method Fréchet derivative Hamiltonian field theory Helmholtz free energy Mathematical physics Lebesgue measure Nonlinear element Early universe Expected value Fermi Gamma-ray Space Telescope Feynman diagrams Nonlinear Norbert Wiener Partition function (statistical mechanics) Legendre transformation Statistical field theory Posterior probability Signal reconstruction Taylor series Hyperprior First-order phase transition Statistical Inference Bayesian statistics Karl G. Jansky Very Large Array Wiener filter Field (physics) Algorithms Kullback-Leibler divergence Maximum a posteriori estimation Variational Bayesian methods Non-Gaussianity Fourier space Cartesian space Gaussian probability distribution Physical field RXTE Shannon Entropy
Link from a Wikipage to an external page	http://ascl.net/1504.018 https://gitlab.mpcdf.mpg.de/ift/nifty http://ascl.net/1505.028 https://github.com/dbuscombe-usgs/pysesa
sameAs	Information field theory Information field theory Information field theory
dbp:wikiPageUsesTemplate	dbt:Reflist
thumbnail	wiki-commons:Special:FilePath/Abell_2219_radio.png?width=300
has abstract	Information field theory (IFT) is a Bayesian statistical field theory relating to signal reconstruction, cosmography, and other related areas. 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. (en)
gold:hypernym	Theory
prov:wasDerivedFrom	wikipedia-en:Information_field_theory?oldid=1102598925&ns=0
page length (characters) of wiki page	31853 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Information_field_theory
is Link from a Wikipage to another Wikipage of	Bayesian inference Statistical inference Kriging Wiener deconvolution Nonparametric regression Signal reconstruction Wiener filter Information theory IFT Spectral density estimation
is Wikipage disambiguates of	IFT
is foaf:primaryTopic of	wikipedia-en:Information_field_theory

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