Divergence-based Mutual Information

Divergence-Based Mutual Information is a mutual information measure defined as the Kullback-Leibler divergence between the joint distribution of two random variables and the product of their marginal distributions, quantifying the statistical dependence between them.

14

Publications

467

Citations

31

Authors

19

Institutions

Publications per year

2017–2026

1

Authors

31

  1. SV
    Sergio Verdú

    Princeton University, United States

    2 publications · 111 citations

    2

    h-index

  2. AK
    Akshay Krishnamurthy

    Microsoft (United States), United States

    2 publications · 96 citations

    2

    h-index

  3. JM
    James M. Robins

    Harvard University, United States

    2 publications · 96 citations

    2

    h-index

  4. KK
    Kirthevasan Kandasamy

    Carnegie Mellon University, United States

    2 publications · 96 citations

    2

    h-index

  5. LW
    Larry Wasserman

    Carnegie Mellon University, United States

    1 publications · 13 citations

    1

    h-index

1–5 of 31

Venues

  1. IEEE Transactions on Information Theory

    3 publications · 103 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 117 citations

    2

    h-index

  3. Medical Image Analysis

    1 publications · 23 citations

    1

    h-index

  4. IEEE Transactions on Signal Processing

    1 publications · 14 citations

    1

    h-index

  5. JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY

    1 publications · 12 citations

    1

    h-index

1–5