Bayesian Semiparametric Models
Bayesian Semiparametric Models is a Bayesian statistical modeling framework that combines parametric and nonparametric components by assigning a prior distribution to both finite-dimensional parameters and infinite-dimensional functions, thereby allowing flexible inference while retaining partial parametric structure.
231
Publications
10.1K
Citations
440
Authors
275
Institutions
Publications per year
2017–2026
30
Parents
231
A semiparametric Bayesian model for randomised block designs
Christopher Bush · Biometrika · 1996 · 310 citations
1–5 of 231
440
11
h-index
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9
h-index
The University of Texas MD Anderson Cancer Center, United States
7 publications · 390 citations
7
h-index
6
h-index
1–5 of 440
275
Duke University
Durham, United States
13 h-index
20 publications
838 citations
Texas A&M University
College Station, United States
10 h-index
24 publications
605 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
10 h-index
23 publications
642 citations
University of South Carolina
Columbia, United States
10 h-index
18 publications
585 citations
The University of Texas MD Anderson Cancer Center
Houston, United States
9 h-index
19 publications
386 citations
The University of Texas at Austin
Austin, United States
9 h-index
12 publications
413 citations
University of Florida
Gainesville, United States
8 h-index
10 publications
173 citations
University of Michigan
Ann Arbor, United States
7 h-index
11 publications
430 citations
1–8 of 275
20 publications · 851 citations
18
h-index
23 publications · 1.3K citations
18
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27 publications · 1.2K citations
18
h-index
CSDA · 10 publications · 184 citations
10
h-index
JCGS · 9 publications · 359 citations
9
h-index
1–5