Mutual Information Estimation

Mutual Information Estimation is a statistical methodology that quantifies the degree of dependence between random variables by estimating the amount of information shared between them, distinguished from related inference techniques through its foundation in information-theoretic principles and entropy measures.

970

Publications

79.4K

Citations

2.4K

Authors

933

Institutions

Publications per year

2017–2026

291

Authors

2.4K

  1. SV
    Sergio Verdú

    Princeton University, United States

    15 publications · 1.3K citations

    14

    h-index

  2. JC
    José C. Prı́ncipe

    University of Florida, United States

    12 publications · 577 citations

    11

    h-index

  3. MS
    Masashi Sugiyama

    Tokyo Institute of Technology, Japan

    10 publications · 302 citations

    10

    h-index

  4. MV
    Michel Verleysen

    UCLouvain, Belgium

    9 publications · 619 citations

    9

    h-index

  5. DE
    Deniz Erdoğmuş

    Northeastern University, United States

    8 publications · 463 citations

    8

    h-index

1–5 of 2.4K

Venues

  1. arXiv (Cornell University)

    Repository · 69 publications · 5.4K citations

    34

    h-index

  2. IEEE Transactions on Information Theory

    39 publications · 3K citations

    26

    h-index

  3. Entropy

    36 publications · 1.5K citations

    21

    h-index

  4. Neurocomputing

    14 publications · 736 citations

    14

    h-index

  5. Neural Computation

    14 publications · 2.7K citations

    13

    h-index

1–5