Probabilistic Reasoning

Probabilistic Reasoning is a formal framework for uncertainty reasoning that employs probability theory to represent and manipulate uncertain knowledge, distinguishing itself by quantifying uncertainty through numerical probabilities and updating beliefs via Bayes’ theorem or related statistical inference methods.

2K

Publications

138.2K

Citations

3.4K

Authors

1.2K

Institutions

Publications per year

2017–2026

353

Authors

3.4K

  1. DD
    Didier Dubois

    Université Toulouse III - Paul Sabatier, France

    23 publications · 3.1K citations

    19

    h-index

  2. RR
    Ronald R. Yager

    Iona College, United States

    18 publications · 846 citations

    17

    h-index

  3. HP
    Henri Prade

    Université Toulouse III - Paul Sabatier, France

    13 publications · 1.1K citations

    13

    h-index

  4. JY
    Joseph Y. Halpern

    Cornell University, United States

    15 publications · 1.3K citations

    13

    h-index

  5. TL
    Thomas Lukasiewicz

    University of Oxford, United Kingdom

    12 publications · 540 citations

    12

    h-index

1–5 of 3.4K

Venues

  1. arXiv (Cornell University)

    Repository · 73 publications · 2.7K citations

    28

    h-index

  2. Artificial Intelligence

    30 publications · 5.4K citations

    25

    h-index

  3. International Journal of Approximate Reasoning

    42 publications · 2.2K citations

    23

    h-index

  4. Journal of the American Statistical Association

    24 publications · 5.8K citations

    21

    h-index

  5. Philosophy of Science

    27 publications · 3.1K citations

    19

    h-index

1–5