Information Divergence

Information Divergence is a measure of discrepancy between two probability distributions that belongs to the class of statistical distance measures, distinguished by its foundation in information theory and its quantification of the relative entropy or inefficiency arising from assuming one distribution over another.

491

Publications

40.8K

Citations

830

Authors

499

Institutions

Publications per year

2017–2026

124

Variants

Information-theoretic Divergence

Authors

830

  1. PH
    Peter Harremoës

    Centrum Wiskunde & Informatica, Netherlands

    11 publications · 2.2K citations

    11

    h-index

  2. IV
    Igor Vajda

    Czech Academy of Sciences, Institute of Information Theory and Automation, Czechia

    11 publications · 954 citations

    11

    h-index

  3. AO
    Alfred O. Hero

    University of Michigan, United States

    10 publications · 546 citations

    10

    h-index

  4. FN
    Frank Nielsen

    Sony Computer Science Laboratories, Japan

    10 publications · 464 citations

    10

    h-index

  5. IJ
    Inder J. Taneja

    Universidade Federal de Santa Catarina, Brazil

    7 publications · 162 citations

    7

    h-index

1–5 of 830

Venues

  1. IEEE Transactions on Information Theory

    53 publications · 13K citations

    33

    h-index

  2. Entropy

    27 publications · 1.5K citations

    20

    h-index

  3. arXiv (Cornell University)

    Repository · 15 publications · 417 citations

    12

    h-index

  4. Information Sciences

    10 publications · 364 citations

    10

    h-index

  5. 7

    h-index

1–5