Informative Priors
Informative Priors is a type of prior distribution in Bayesian model building that incorporates substantial, specific knowledge about the parameters, thereby distinguishing itself from non-informative or weakly informative priors by significantly influencing the posterior inference through concentrated probability mass on plausible parameter values.
737
Publications
59.9K
Citations
1.9K
Authors
915
Institutions
Publications per year
2017–2026
249
737
1–5 of 737
1.9K
University of North Carolina at Chapel Hill, United States
8 publications · 965 citations
8
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7
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6
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6
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5
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1–5 of 1.9K
915
Columbia University
New York, United States
14 h-index
29 publications
18.3K citations

University of Toronto
Toronto, Canada
14 h-index
23 publications
1.8K citations
University College London
London, United Kingdom
13 h-index
27 publications
2.4K citations
Duke University
Durham, United States
11 h-index
30 publications
2.9K citations
University of Connecticut
Storrs, United States
10 h-index
15 publications
1.4K citations
University of California, Davis
Davis, United States
10 h-index
16 publications
595 citations
University of Cambridge
Cambridge, United Kingdom
10 h-index
28 publications
1.2K citations
University of California, Los Angeles
Los Angeles, United States
10 h-index
21 publications
625 citations
1–8 of 915
28 publications · 2.1K citations
21
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24 publications · 1.9K citations
19
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15 publications · 10.4K citations
14
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15 publications · 591 citations
13
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Repository · 12 publications · 534 citations
12
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1–5