Mixture Priors
Mixture Priors is a prior specification method in Bayesian statistics that belongs to the class of hierarchical priors and is distinguished by its construction as a weighted combination of multiple component distributions to flexibly represent complex prior beliefs or account for uncertainty in prior knowledge.
75
Publications
6K
Citations
176
Authors
107
Institutions
Publications per year
2017–2026
17
75
Mixtures of <i>g</i> Priors for Bayesian Variable Selection
Feng Liang, Rui Paulo, Germán Molina et al. · Journal of the American Statistical Association · 2008 · 1.1K citations
Dirichlet–Laplace Priors for Optimal Shrinkage
Anirban Bhattacharya, Debdeep Pati, Natesh S. Pillai et al. · Journal of the American Statistical Association · 2014 · 428 citations · Full text
Random Effects Selection in Linear Mixed Models
Zhen Chen, David B. Dunson · Biometrics · 2003 · 240 citations
1–5 of 75
176
5
h-index
4
h-index
3
h-index
3
h-index
2
h-index
1–5 of 176
107
Texas A&M University
College Station, United States
5 h-index
11 publications
477 citations
The University of Texas MD Anderson Cancer Center
Houston, United States
4 h-index
7 publications
361 citations
University of California, Davis
Davis, United States
2 h-index
4 publications
138 citations
National Institute of Environmental Health Sciences
Durham, United States
2 h-index
3 publications
159 citations

University of Turin
Turin, Italy
2 h-index
4 publications
212 citations
University of Pennsylvania
Philadelphia, United States
2 h-index
3 publications
578 citations
University of Cambridge
Cambridge, United Kingdom
2 h-index
2 publications
485 citations
Novartis (Switzerland)
Basel, Switzerland
2 h-index
3 publications
972 citations
1–8 of 107
7 publications · 1K citations
7
h-index
5 publications · 358 citations
5
h-index
5 publications · 1.9K citations
5
h-index
4 publications · 190 citations
4
h-index
JCGS · 3 publications · 86 citations
3
h-index
1–5