Bayesian Variance Component Estimation is a statistical method that uses Bayesian inference to estimate variance components by incorporating prior distributions over variance parameters and updating them with observed data to obtain posterior distributions, thereby distinguishing itself from classical frequentist approaches through its probabilistic treatment of variance parameters.
52
Publications
2.6K
Citations
112
Authors
79
Institutions
Publications per year
2017–2026
6
Parents
Publications
52
1–5 of 52
Authors
112
- GC
4
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- TL
3
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1–5 of 112
Institutions
79
University of Wisconsin–Madison
Madison, United States
3 h-index
4 publications
90 citations
Virginia Tech
Blacksburg, United States
3 h-index
7 publications
262 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
3 publications
117 citations
University of New England
Armidale, Australia
2 h-index
4 publications
220 citations
St Mary's Hospital
Kettering, United Kingdom
2 h-index
2 publications
101 citations
Harvard University
Cambridge, United States
2 h-index
2 publications
101 citations
Harvard University Press
Cambridge, United States
2 h-index
2 publications
182 citations
University of Minnesota
Minneapolis, United States
2 h-index
2 publications
52 citations
1–8 of 79
Venues
- Journal of the American Statistical Association
5 publications · 304 citations
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- Technometrics
5 publications · 113 citations
5
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- Biometrika
5 publications · 738 citations
5
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- Genetics Selection Evolution
GSE · 4 publications · 170 citations
4
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- Journal of Dairy Science
JDS · 3 publications · 286 citations
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