Stick-breaking Processes

Stick-Breaking Processes is a stochastic process in Bayesian nonparametrics that generates a discrete probability distribution by recursively partitioning the unit interval into diminishing segments, where each segment represents the weight of a component in an infinite mixture model.

79

Publications

10.6K

Citations

145

Authors

79

Institutions

Publications per year

2017–2026

8

Variants

Stick-breaking Process

Authors

145

  1. DB
    David B. Dunson

    Duke University, United States

    12 publications · 1.6K citations

    12

    h-index

  2. LC
    Lawrence Carin

    Duke University, United States

    10 publications · 991 citations

    10

    h-index

  3. JP
    John Paisley

    Columbia University, United States

    6 publications · 773 citations

    6

    h-index

  4. YW
    Yee Whye Teh

    University of Oxford, United Kingdom

    5 publications · 4K citations

    5

    h-index

  5. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    5 publications · 3.8K citations

    5

    h-index

1–5 of 145

Venues

  1. Journal of the American Statistical Association

    11 publications · 6.4K citations

    11

    h-index

  2. Bayesian Analysis

    9 publications · 609 citations

    9

    h-index

  3. arXiv (Cornell University)

    Repository · 8 publications · 312 citations

    8

    h-index

  4. Journal of Applied Probability

    JAP · 4 publications · 164 citations

    4

    h-index

  5. Biometrika

    4 publications · 525 citations

    4

    h-index

1–5