Probabilistic Representation

Probabilistic Representation is a formal framework within uncertainty representation that characterizes uncertain knowledge through probability distributions, distinguishing itself by quantifying uncertainty in terms of measurable likelihoods over well-defined sample spaces.

817

Publications

53.5K

Citations

1.7K

Authors

718

Institutions

Publications per year

2017–2026

145

Authors

1.7K

  1. DD
    Didier Dubois

    Université Toulouse III - Paul Sabatier, France

    10 publications · 598 citations

    10

    h-index

  2. SD
    Sébastien Destercke

    Centre National de la Recherche Scientifique, France

    8 publications · 288 citations

    8

    h-index

  3. DH
    David Heckerman

    Microsoft (United States), United States

    8 publications · 1.3K citations

    8

    h-index

  4. DK
    Daphne Koller

    Stanford University, United States

    7 publications · 950 citations

    7

    h-index

  5. DS
    Dan Suciu

    University of Washington, United States

    6 publications · 998 citations

    6

    h-index

1–5 of 1.7K

Venues

  1. arXiv (Cornell University)

    Repository · 34 publications · 1.1K citations

    19

    h-index

  2. International Journal of Approximate Reasoning

    22 publications · 1.5K citations

    17

    h-index

  3. 11

    h-index

  4. Fuzzy Sets and Systems

    11 publications · 443 citations

    11

    h-index

  5. Information Sciences

    10 publications · 265 citations

    10

    h-index

1–5