Bayesian Semiparametric Models

Bayesian Semiparametric Models is a Bayesian statistical modeling framework that combines parametric and nonparametric components by assigning a prior distribution to both finite-dimensional parameters and infinite-dimensional functions, thereby allowing flexible inference while retaining partial parametric structure.

231

Publications

10.1K

Citations

440

Authors

275

Institutions

Publications per year

2017–2026

30

Authors

440

  1. TH
    Timothy Hanson

    University of South Carolina, United States

    11 publications · 529 citations

    11

    h-index

  2. BK
    Bani K. Mallick

    Texas A&M University, United States

    10 publications · 424 citations

    10

    h-index

  3. DB
    David B. Dunson

    Duke University, United States

    9 publications · 448 citations

    9

    h-index

  4. PM
    Peter Müller

    The University of Texas MD Anderson Cancer Center, United States

    7 publications · 390 citations

    7

    h-index

  5. PG
    Pulak Ghosh

    Indian Institute of Management Bangalore, India

    6 publications · 368 citations

    6

    h-index

1–5 of 440

Venues

  1. Statistics in Medicine

    20 publications · 851 citations

    18

    h-index

  2. Journal of the American Statistical Association

    23 publications · 1.3K citations

    18

    h-index

  3. Biometrics

    27 publications · 1.2K citations

    18

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 10 publications · 184 citations

    10

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 9 publications · 359 citations

    9

    h-index

1–5