Bayesian Regression Trees

Bayesian Regression Trees is a probabilistic nonparametric regression model that uses a tree-based structure to partition the predictor space and assigns posterior distributions over tree configurations and leaf node parameters through Bayesian inference, distinguishing itself from classical regression trees by quantifying uncertainty in both structure and predictions.

102

Publications

8.6K

Citations

317

Authors

214

Institutions

Publications per year

2017–2026

68

Authors

317

  1. RE
    Robert E. McCulloch

    University of Chicago, United States

    9 publications · 1.7K citations

    9

    h-index

  2. HC
    Hugh Chipman

    Acadia University, Canada

    8 publications · 1.5K citations

    8

    h-index

  3. RS
    Rodney Sparapani

    Medical College of Wisconsin, United States

    8 publications · 497 citations

    8

    h-index

  4. EI
    Edward I. George

    University of Pennsylvania, United States

    7 publications · 1.5K citations

    7

    h-index

  5. FH
    Florian Huber

    University of Salzburg, Austria

    5 publications · 195 citations

    5

    h-index

1–5 of 317

Venues

  1. Statistics in Medicine

    8 publications · 466 citations

    8

    h-index

  2. Bayesian Analysis

    5 publications · 451 citations

    5

    h-index

  3. PLoS ONE

    4 publications · 129 citations

    4

    h-index

  4. Statistical Methods in Medical Research

    4 publications · 193 citations

    4

    h-index

  5. arXiv (Cornell University)

    Repository · 4 publications · 106 citations

    4

    h-index

1–5