Posterior Asymptotics
Posterior Asymptotics is a theoretical framework in Bayesian statistics that characterizes the limiting behavior of posterior distributions as the sample size grows, distinguishing itself by analyzing convergence rates and consistency properties under increasingly large data regimes.
325
Publications
17.5K
Citations
441
Authors
255
Institutions
Publications per year
2017–2026
23
Parents
325
1–5 of 325
441
6
h-index
5
h-index
5
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5
h-index
University at Buffalo, State University of New York, United States
4 publications · 317 citations
4
h-index
1–5 of 441
255
Purdue University West Lafayette
West Lafayette, United States
11 h-index
13 publications
745 citations
University of Florida
Gainesville, United States
9 h-index
18 publications
531 citations

Université Paris Dauphine-PSL
Paris, France
7 h-index
8 publications
656 citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
10 publications
773 citations
The University of Tokyo
Tokyo, Japan
6 h-index
9 publications
153 citations
Princeton University
Princeton, United States
5 h-index
5 publications
227 citations
Michigan State University
East Lansing, United States
5 h-index
5 publications
103 citations

Indian Statistical Institute
Kolkata, India
5 h-index
12 publications
2.6K citations
1–8 of 255
46 publications · 3K citations
25
h-index
22 publications · 1.9K citations
18
h-index
15 publications · 797 citations
15
h-index
12 publications · 1.1K citations
11
h-index
12 publications · 172 citations
11
h-index
1–5