Bayesian Experimentation is a statistical methodology for designing and analyzing experiments that uses Bayesian inference to sequentially update the probability of hypotheses as data are collected, distinguishing it from frequentist approaches by incorporating prior knowledge and yielding direct probabilistic interpretations of outcomes.
47
Publications
2.6K
Citations
101
Authors
56
Institutions
Publications per year
2017–2026
10
Parents
Publications
47
1–5 of 47
Authors
101
- MA
3
h-index
- KY
2
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- DM
2
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- MW
2
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- JG
2
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1–5 of 101
Institutions
56
California Institute of Technology
Pasadena, United States
3 h-index
6 publications
964 citations
University of Washington
Seattle, United States
2 h-index
6 publications
137 citations
University of Amsterdam
Amsterdam, The Netherlands
2 h-index
4 publications
894 citations
University of Michigan
Ann Arbor, United States
2 h-index
2 publications
511 citations
University of Missouri
Columbia, United States
1 h-index
2 publications
28 citations
University of Colorado Boulder
Boulder, United States
1 h-index
1 publications
17 citations
University of Maryland, College Park
College Park, United States
1 h-index
1 publications
28 citations
National Cheng Kung University
Tainan City, Taiwan
1 h-index
1 publications
15 citations
1–8 of 56
Venues
- Journal of the American Statistical Association
4 publications · 476 citations
4
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- Journal of Statistical Planning and Inference
2 publications · 32 citations
2
h-index
- Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment
2 publications · 22 citations
2
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- Philosophy of Science
1 publications · 11 citations
1
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- University of Minnesota Digital Conservancy (University of Minnesota)
Repository · 1 publications · 31 citations
1
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1–5