Online Bayesian Inference
Online Bayesian Inference is a methodological framework within Bayesian statistics that enables sequential updating of posterior distributions as new data arrive in a continuous or iterative manner, distinguishing it from batch or offline inference by its adaptive, real-time integration of evidence.
154
Publications
7.2K
Citations
460
Authors
207
Institutions
Publications per year
2017–2026
54
Parents
154
Pairwise ranking aggregation in a crowdsourced setting
Xi Chen, Paul N. Bennett, Kevyn Collins‐Thompson et al. · 2013 · 307 citations
1–5 of 154
460
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Indiana University – Purdue University Indianapolis, United States
2 publications · 21 citations
2
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2
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1–5 of 460
207

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
6 h-index
14 publications
467 citations
Microsoft (United States)
Redmond, United States
6 h-index
14 publications
1.2K citations
Tsinghua University
Beijing, China
6 h-index
13 publications
472 citations
University of California, Berkeley
Berkeley, United States
5 h-index
10 publications
1.1K citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
10 publications
1.1K citations
Shandong Institute of Automation
Jinan, China
3 h-index
9 publications
594 citations
University College London
London, United Kingdom
3 h-index
11 publications
1.1K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
3 h-index
7 publications
353 citations
1–8 of 207
Repository · 10 publications · 243 citations
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5 publications · 435 citations
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Conference · 4 publications · 117 citations
4
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3 publications · 290 citations
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3 publications · 178 citations
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