Shannon Entropy

Shannon Entropy is an information measure that quantifies the average level of uncertainty or information content associated with the outcomes of a random variable by computing the expected value of the logarithmic probabilities of its possible states.

4.1K

Publications

219.2K

Citations

9.8K

Authors

3.2K

Institutions

Publications per year

2017–2026

1.5K

Variants

Information Entropy

Authors

9.8K

  1. RP
    Robin P. Sagar

    Universidad Autónoma Metropolitana, Mexico

    20 publications · 749 citations

    17

    h-index

  2. JS
    J. S. Dehesa

    Universidad de Granada, Spain

    20 publications · 865 citations

    16

    h-index

  3. YD
    Yong Deng

    University of Electronic Science and Technology of China, China

    18 publications · 972 citations

    16

    h-index

  4. RF
    Roman F. Nalewajski

    Jagiellonian University, Poland

    14 publications · 384 citations

    14

    h-index

  5. HN
    Hamidreza Namazi

    Monash University Malaysia, Malaysia

    20 publications · 444 citations

    14

    h-index

1–5 of 9.8K

Venues

  1. IEEE Transactions on Information Theory

    155 publications · 26.4K citations

    62

    h-index

  2. Entropy

    242 publications · 8.9K citations

    46

    h-index

  3. Physical Review A

    PRA · 45 publications · 3.5K citations

    32

    h-index

  4. arXiv (Cornell University)

    Repository · 60 publications · 2.6K citations

    26

    h-index

  5. Physical Review Letters

    PRL · 33 publications · 3.7K citations

    26

    h-index

1–5