Posterior Updating
Posterior Updating is a Bayesian inference procedure that belongs to the class of sequential learning methods, specifically characterized by the iterative revision of a parameter's probability distribution through the application of Bayes' theorem as new observational data becomes available.
177
Publications
10.4K
Citations
434
Authors
223
Institutions
Publications per year
2017–2026
61
Parents
177
Bayesian and non-bayesian evidential updating
Henry E. Kyburg · Artificial Intelligence · 1987 · 316 citations
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223

Beijing University of Technology
Beijing, China
5 h-index
24 publications
1.2K citations

University of Liverpool
Liverpool, United Kingdom
5 h-index
9 publications
881 citations
Stanford University
Stanford, United States
4 h-index
7 publications
166 citations
The Ohio State University
Columbus, United States
4 h-index
8 publications
350 citations
University of Oxford
Oxford, United Kingdom
4 h-index
12 publications
835 citations
Tongji University
Shanghai, China
3 h-index
4 publications
135 citations

Hefei University of Technology
Hefei, China
3 h-index
7 publications
435 citations
California Institute of Technology
Pasadena, United States
3 h-index
3 publications
186 citations
1–8 of 223
MSSP · 8 publications · 589 citations
8
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6 publications · 363 citations
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5 publications · 429 citations
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JSV · 4 publications · 245 citations
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4 publications · 112 citations
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