Conditional Probability Models

Conditional Probability Models is a probabilistic modeling framework that specifies the distribution of a target variable given the values of other variables, distinguishing itself by formalizing dependencies through conditional distributions rather than joint or marginal distributions.

2.2K

Publications

209.7K

Citations

5.6K

Authors

1.8K

Institutions

Publications per year

2017–2026

486

Variants

Conditional Probabilistic Models

Authors

5.6K

  1. DE
    David E. Over

    Durham University, United Kingdom

    12 publications · 735 citations

    11

    h-index

  2. AG
    Angelo Gilio

    Sapienza University of Rome, Italy

    9 publications · 306 citations

    9

    h-index

  3. RS
    Romano Scozzafava

    Sapienza University of Rome, Italy

    8 publications · 351 citations

    8

    h-index

  4. PK
    Philip K. Hopke

    Clarkson University, United States

    8 publications · 587 citations

    8

    h-index

  5. MJ
    Marek J. Drużdżel

    University of Pittsburgh, United States

    7 publications · 174 citations

    7

    h-index

1–5 of 5.6K

Venues

  1. Journal of the American Statistical Association

    32 publications · 14.8K citations

    25

    h-index

  2. arXiv (Cornell University)

    Repository · 55 publications · 5.9K citations

    23

    h-index

  3. Biometrics

    17 publications · 986 citations

    15

    h-index

  4. Water Resources Research

    14 publications · 950 citations

    13

    h-index

  5. IEEE Transactions on Power Systems

    12 publications · 771 citations

    12

    h-index

1–5