Prior Regularization
Prior Regularization is a methodological approach in Bayesian inference that belongs to the class of prior specification techniques, distinguished by its incorporation of regularization principles to constrain prior distributions in order to improve model generalization and robustness.
265
Publications
15.8K
Citations
764
Authors
398
Institutions
Publications per year
2017–2026
101
Parents
265
1–5 of 265
764
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398

Centre National de la Recherche Scientifique
Paris, France
9 h-index
18 publications
1.8K citations
Dartmouth College
Hanover, United States
9 h-index
29 publications
2.1K citations
Tsinghua University
Beijing, China
7 h-index
13 publications
937 citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
9 publications
443 citations

Chinese Academy of Sciences
Beijing, China
5 h-index
11 publications
357 citations
Stony Brook University
Stony Brook, United States
5 h-index
7 publications
409 citations
University of Chicago
Chicago, United States
4 h-index
5 publications
187 citations

Zhejiang University
Hangzhou, China
4 h-index
4 publications
182 citations
1–8 of 398
Repository · 20 publications · 764 citations
16
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9 publications · 631 citations
9
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6 publications · 367 citations
6
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5 publications · 613 citations
5
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Conference · 4 publications · 160 citations
4
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1–5