Scale Priors
Scale Priors is a methodological construct in Bayesian statistics that specifies prior distributions on the scale parameters of a model to reflect uncertainty about the magnitude of variation while maintaining invariance under rescaling transformations.
20
Publications
1.6K
Citations
56
Authors
32
Institutions
Publications per year
2017–2026
6
20
Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging
Tao Huang, Weisheng Dong, Xin Yuan et al. · 2021 · 169 citations
1–5 of 20
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Industrial Technology Research Institute, Taiwan
1 publications · 12 citations
1
h-index
1–5 of 56
32
University of Washington
Seattle, United States
2 h-index
2 publications
38 citations

University of Warwick
Coventry, United Kingdom
2 h-index
3 publications
99 citations
Adobe Systems (United States)
San Jose, United States
2 h-index
4 publications
131 citations
University of Puerto Rico at Río Piedras
San Juan, Puerto Rico
1 h-index
2 publications
62 citations
Stanford University
Stanford, United States
1 h-index
4 publications
88 citations
National University of Singapore
Singapore, Singapore
1 h-index
3 publications
105 citations

University of Leicester
Leicester, United Kingdom
1 h-index
1 publications
516 citations
University of Michigan
Ann Arbor, United States
1 h-index
2 publications
38 citations
1–8 of 32
2 publications · 501 citations
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OB · 1 publications · 10 citations
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1 publications · 48 citations
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1 publications · 14 citations
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1 publications · 17 citations
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