Bayesian Topic Models

Bayesian Topic Models is a class of probabilistic generative models for discovering latent thematic structures in document collections through Bayesian inference over discrete topic assignments, distinguished by their joint modeling of documents as mixtures of topics and words as draws from topic-specific word distributions, with priors imposed on the latent variables to support principled uncertainty quantification and sparse, interpretable topic representations.

183

Publications

17K

Citations

498

Authors

214

Institutions

Publications per year

2017–2026

58

Authors

498

  1. DM
    David M. Blei

    Princeton University, United States

    9 publications · 1.6K citations

    9

    h-index

  2. JZ
    Jun Zhu

    Tsinghua University, China

    8 publications · 417 citations

    8

    h-index

  3. LC
    Lawrence Carin

    Duke University, United States

    6 publications · 190 citations

    6

    h-index

  4. MZ
    Mingyuan Zhou

    The University of Texas at Austin, United States

    5 publications · 132 citations

    5

    h-index

  5. CC
    Changyou Chen

    Duke University, United States

    4 publications · 104 citations

    4

    h-index

1–5 of 498

Venues

  1. arXiv (Cornell University)

    Repository · 21 publications · 949 citations

    16

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 7 publications · 269 citations

    7

    h-index

  3. 5

    h-index

  4. Neural Information Processing Systems

    Conference · 3 publications · 992 citations

    3

    h-index

  5. ACM Transactions on Knowledge Discovery from Data

    TKDD · 3 publications · 62 citations

    3

    h-index

1–5