Nonparametric Hierarchical Models
Nonparametric Hierarchical Models is a statistical modeling framework that extends hierarchical models by allowing the number of parameters to adapt to data complexity through nonparametric Bayesian methods, thereby avoiding fixed parametric assumptions about the underlying distributional form.
80
Publications
3.9K
Citations
209
Authors
108
Institutions
Publications per year
2017–2026
13
Parents
80
Nonparametric hierarchical Bayes via sequential imputations
Jun S. Liu · The Annals of Statistics · 1996 · 195 citations · Full text
Practical Nonparametric and Semiparametric Bayesian Statistics
Eric R. Ziegel, Dipak Dey, P. Müller et al. · Technometrics · 1999 · 190 citations
1–5 of 80
209
Istituto di Matematica Applicata e Tecnologie Informatiche, Italy
4 publications · 88 citations
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3
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3
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3
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1–5 of 209
108
Carnegie Mellon University
Pittsburgh, United States
5 h-index
10 publications
538 citations
The University of Texas at Austin
Austin, United States
5 h-index
5 publications
171 citations
Duke University
Durham, United States
4 h-index
5 publications
206 citations
The University of Texas MD Anderson Cancer Center
Houston, United States
4 h-index
11 publications
550 citations
University of California, Berkeley
Berkeley, United States
4 h-index
8 publications
452 citations

UNSW Sydney
Sydney, Australia
3 h-index
5 publications
256 citations
University of Illinois Urbana-Champaign
Urbana, United States
3 h-index
5 publications
793 citations
Los Alamos National Laboratory
Los Alamos, United States
3 h-index
3 publications
122 citations
1–8 of 108
11 publications · 895 citations
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Repository · 4 publications · 80 citations
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3 publications · 63 citations
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2 publications · 80 citations
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2 publications · 27 citations
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