Dependent Dirichlet Processes

Dependent Dirichlet Processes is a stochastic process that extends the Dirichlet process by allowing the probability distribution indexed by covariates or time to evolve while maintaining the nonparametric Bayesian structure, thereby modeling collections of related random measures with partial exchangeability and dependence across groups or domains.

41

Publications

2.4K

Citations

95

Authors

50

Institutions

Publications per year

2017–2026

7

Authors

95

  1. FA
    Fernando A. Quintana

    Pontificia Universidad Católica de Chile, Chile

    4 publications · 178 citations

    4

    h-index

  2. PM
    Peter Müller

    The University of Texas MD Anderson Cancer Center, United States

    4 publications · 592 citations

    4

    h-index

  3. GL
    Gary L. Rosner

    Johns Hopkins University, United States

    3 publications · 518 citations

    3

    h-index

  4. MD
    Maria De Iorio

    Imperial College London, United Kingdom

    3 publications · 518 citations

    3

    h-index

  5. AK
    Athanasios Kottas

    University of California, Santa Cruz, United States

    3 publications · 180 citations

    3

    h-index

1–5 of 95

Venues

  1. 7

    h-index

  2. Bayesian Analysis

    3 publications · 172 citations

    3

    h-index

  3. 2

    h-index

  4. eScholarship (California Digital Library)

    Repository · 2 publications · 41 citations

    2

    h-index

  5. The Annals of Applied Statistics

    AOAS · 2 publications · 34 citations

    2

    h-index

1–5