Adaptive Bayesian Learning
Adaptive Bayesian Learning is a Bayesian inference framework that iteratively refines probabilistic beliefs through sequential data acquisition, distinguished by its capacity to dynamically adjust prior distributions and model structures in response to evolving evidence.
118
Publications
7.1K
Citations
450
Authors
243
Institutions
Publications per year
2017–2026
51
Parents
118
Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip
Stefano Paesani, Antonio A. Gentile, Raffaele Santagati et al. · Physical Review Letters · 2017 · 184 citations · Full text
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Korea Advanced Institute of Science and Technology, South Korea
2 publications · 75 citations
2
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1–5 of 450
243

University of Hong Kong
Pok Fu Lam, Hong Kong
4 h-index
6 publications
373 citations
Tsinghua University
Beijing, China
3 h-index
8 publications
301 citations
The Ohio State University
Columbus, United States
3 h-index
8 publications
292 citations
Hong Kong Baptist University
Hong Kong, Hong Kong
3 h-index
6 publications
191 citations

University of Exeter
Exeter, United Kingdom
2 h-index
5 publications
301 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
3 publications
225 citations

Oak Ridge National Laboratory
Oak Ridge, United States
2 h-index
1 publications
23 citations
University of Oxford
Oxford, United Kingdom
2 h-index
2 publications
74 citations
1–8 of 243
5 publications · 2.3K citations
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Repository · 3 publications · 88 citations
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2 publications · 29 citations
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2 publications · 81 citations
2
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Repository · 2 publications · 116 citations
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