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Mutual information

In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the "amount of information" (in units such as shannons (bits), nats or hartleys) obtained about one random variable by observing the other random variable. The…

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Mutual information

Nodes138
Edges137
Triples229
Avg. degree1.99
Density0.014493
Components1

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Mutual information

Top relations

related to References · 141
Mutual information → Aghagolzadeh, American Mathematical Society Translations, Amsterdam, An, An Elementary Introduction, Andre, Annual Meeting, Applications, Archived, Artificial Intelligence, Association, Athanasios Papoulis, Atsumi, Baudot, Bennequin, Bibcode, Biswajit, Bjorn Samuelsson, Calculation, Cambridge University Press
is a · 7
Mutual information → form of weighted KL-Divergence, hartley, Kullback, measure of the inherent dependence expressed in the joint distribution of X, nat, same as the uncertainty contained in Y, shannon
related to Absolute mutual information · 7
Mutual information → Approximations, Cilibrasi, Kolmogorov, Li, To, Using, Vitányi
related to Adjusted mutual information · 7
Mutual information → AMI, MI, One, Rand, The, The AMI, What
related to Motivation · 7
Mutual information → As, At, For, Intuitively, It, Mutual, This
related to Bayesian estimation of mutual information · 6
Mutual information → Bayesian, Besides, If, See, Subsequent, The
related to For discrete data · 6
Mutual information → G-test, In, Mutual, Other, Pearson's, When
related to Linear correlation · 6
Mutual information → Gaussian, Gel'fand, However, The, Unlike, Yaglom
related to Definition · 5
Mutual information → If, KL, Kullback, Leibler, Let
related to Interaction information · 5
Mutual information → Hu Kuo Ting, Interaction, McGill, Several, The

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Important terminology

information mutual displaystyle variables random used entropy one variable joint two distribution also probability doi 10 measure theory correlation discrete

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Mutual informationis anat0.90text
Mutual informationis ashannon0.90text
Mutual informationis ahartley0.90text
Mutual informationis asame as the uncertainty contained in Y0.90text
Mutual informationis ameasure of the inherent dependence expressed in the joint distribution of X0.90text
Mutual informationis aKullback0.90text
Mutual informationis aform of weighted KL-Divergence0.90text
shannonsinstance ofin units0.80text
Mutual informationhas applicationIn0.60section
Mutual informationhas applicationExamples0.60section
Mutual informationhas applicationFor0.60section
Mutual informationrelated to Absolute mutual informationUsing0.60section

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