Mixture Prior Estimation

Mixture Prior Estimation is a statistical method in empirical Bayes analysis that belongs to the class of prior estimation techniques and is distinguished by its use of a finite or infinite mixture distribution to model the prior, allowing for greater flexibility in capturing heterogeneity and multimodality in the underlying parameter space.

73

Publications

3.8K

Citations

172

Authors

90

Institutions

Publications per year

2017–2026

9

Authors

172

  1. JH
    James H. Albert

    Bowling Green State University, United States

    4 publications · 93 citations

    4

    h-index

  2. AK
    Arjun K. Gupta

    Bowling Green State University, United States

    3 publications · 77 citations

    3

    h-index

  3. EI
    Edward I. George

    University of Pennsylvania, United States

    3 publications · 1.3K citations

    3

    h-index

  4. DM
    D. M. Titterington

    University of Glasgow, United Kingdom

    2 publications · 93 citations

    2

    h-index

  5. JE
    Jim E. Griffin

    University of Kent, United Kingdom

    2 publications · 62 citations

    2

    h-index

1–5 of 172

Venues

  1. 5

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  2. The Annals of Statistics

    4 publications · 340 citations

    4

    h-index

  3. 3

    h-index

  4. 2

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  5. Biometrika

    2 publications · 35 citations

    2

    h-index

1–5