Max-entropy

Max-Entropy is a principle in information theory that identifies the probability distribution with the greatest entropy among all distributions satisfying given constraints, thereby representing maximal uncertainty consistent with available information.

698

Publications

56.3K

Citations

1.1K

Authors

619

Institutions

Publications per year

2017–2026

87

Variants

Maximum Entropy

Authors

1.1K

  1. AT
    Aldo Tagliani

    University of Trento, Italy

    9 publications · 216 citations

    9

    h-index

  2. RR
    Renato Renner

    ETH Zurich, Switzerland

    7 publications · 1.2K citations

    7

    h-index

  3. AG
    Amos Golan

    American University, United States

    5 publications · 250 citations

    5

    h-index

  4. SK
    S. Kullback

    George Washington University, United States

    5 publications · 2.8K citations

    5

    h-index

  5. GJ
    George J. Klir

    Binghamton University, United States

    5 publications · 242 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. 17

    h-index

  2. IEEE Transactions on Information Theory

    18 publications · 2.2K citations

    16

    h-index

  3. Entropy

    21 publications · 843 citations

    15

    h-index

  4. arXiv (Cornell University)

    Repository · 17 publications · 848 citations

    14

    h-index

  5. International Journal of Approximate Reasoning

    12 publications · 577 citations

    11

    h-index

1–5