Probabilistic Programming

Probabilistic Programming is a computational framework that extends general-purpose programming languages with primitives for defining and performing probabilistic inference over expressive probabilistic models, thereby enabling the specification of complex uncertainty representations and reasoning processes through stochastic simulations and Bayesian updating.

593

Publications

55.6K

Citations

1.2K

Authors

495

Institutions

Publications per year

2017–2026

204

Authors

1.2K

  1. ND
    Noah D. Goodman

    Stanford University, United States

    18 publications · 1.7K citations

    14

    h-index

  2. LD
    Luc De Raedt

    KU Leuven, Belgium

    15 publications · 1.6K citations

    13

    h-index

  3. VK
    Vikash K. Mansinghka

    Massachusetts Institute of Technology, United States

    15 publications · 1.2K citations

    12

    h-index

  4. GH
    Guohe Huang

    University of Regina, Canada

    11 publications · 371 citations

    11

    h-index

  5. JK
    Joost-Pieter Katoen

    RWTH Aachen University, Germany

    11 publications · 363 citations

    11

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 70 publications · 4.3K citations

    30

    h-index

  2. Proceedings of the ACM on Programming Languages

    PACMPL · 27 publications · 869 citations

    19

    h-index

  3. ACM SIGPLAN Notices

    17 publications · 382 citations

    13

    h-index

  4. Theory and Practice of Logic Programming

    10 publications · 733 citations

    10

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 9 publications · 304 citations

    9

    h-index

1–5