Prior-likelihood Models
Prior-Likelihood Models is a formal decision-theoretic framework that integrates subjective prior beliefs with probabilistic likelihood assessments to model choice behavior under ambiguity, distinguishing itself from other models by its reliance on Bayesian updating to resolve uncertainty when objective probabilities are unknown.
27
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48
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2017–2026
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Noise2Noise: Learning Image Restoration without Clean Data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren et al. · arXiv (Cornell University) · 2018 · 988 citations · Full text
Noise2Noise: Learning image restoration without clean data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren et al. · Aaltodoc (Aalto University) · 2018 · 442 citations · Full text
Credible Means are exact Bayesian for Exponential Families
William S. Jewell · Astin Bulletin · 1974 · 148 citations · Full text
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Northwestern University
Evanston, United States
3 h-index
6 publications
338 citations
Nvidia (United Kingdom)
Reading, United Kingdom
2 h-index
12 publications
8.6K citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
3 publications
139 citations
Humboldt-Universität zu Berlin
Berlin, Germany
2 h-index
4 publications
188 citations
University of California, Berkeley
Berkeley, United States
2 h-index
2 publications
184 citations

University of Tübingen
Tübingen, Germany
2 h-index
6 publications
107 citations
Bernstein Center for Computational Neuroscience Berlin
Berlin, Germany
2 h-index
5 publications
260 citations

Shirley Ryan AbilityLab
Chicago, United States
2 h-index
4 publications
152 citations
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Repository · 2 publications · 999 citations
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Repository · 1 publications · 48 citations
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TPAMI · 1 publications · 92 citations
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1 publications · 112 citations
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1 publications · 16 citations
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