Mixture Model Averaging

Mixture Model Averaging is a Bayesian model averaging technique that combines predictive distributions from multiple candidate models using a mixture distribution over model-specific parameters, where the mixing weights are determined by posterior model probabilities, thereby accounting for model uncertainty in inference and prediction.

5

Publications

79

Citations

8

Authors

9

Institutions

Publications per year

2017–2026

2

Authors

8

  1. DM
    David Moreau

    University of Auckland, New Zealand

    1 publications · 15 citations

    1

    h-index

  2. WL
    Wei Long

    Tulane University, United States

    1 publications · 17 citations

    1

    h-index

  3. GL
    Guannan Liu

    China University of Mining and Technology, China

    1 publications · 17 citations

    1

    h-index

  4. XZ
    Xinyu Zhang

    Chinese Academy of Sciences, China

    1 publications · 17 citations

    1

    h-index

  5. MC
    Michael C. Corballis

    University of Auckland, New Zealand

    1 publications · 15 citations

    1

    h-index

1–5 of 8

Venues

  1. Journal of Experimental Psychology General

    1 publications · 15 citations

    1

    h-index

  2. Econometric Theory

    1 publications · 17 citations

    1

    h-index

  3. Advances in Data Analysis and Classification

    1 publications · 19 citations

    1

    h-index

1–3