Robust Priors
Robust Priors is a methodological framework in Bayesian statistics that integrates prior distributions designed to maintain inferential stability and validity across a wide range of model specifications and data conditions, thereby distinguishing it from conventional prior specifications sensitive to assumed parametric forms or outlying observations.
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Publications
3.3K
Citations
108
Authors
75
Institutions
Publications per year
2017–2026
9
Variants
Robust Prior Distributions
Parents
56
RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors
Ruiqi Wu, Zheng-Peng Duan, Chunle Guo et al. · 2023 · 197 citations
Bayes' Theorem for Choquet Capacities
Larry Wasserman, Joseph B. Kadane · The Annals of Statistics · 1990 · 162 citations · Full text
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University of Pennsylvania
Philadelphia, United States
3 h-index
3 publications
417 citations

University of Alberta
Edmonton, Canada
2 h-index
5 publications
62 citations
Decision Sciences (United States)
Fort Walton Beach, United States
2 h-index
2 publications
69 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
2 publications
221 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
5 publications
816 citations

Universidad de Granada
Granada, Spain
2 h-index
2 publications
46 citations
Centre for Economic Policy Research
London, United Kingdom
2 h-index
2 publications
76 citations
Istituto di Matematica Applicata e Tecnologie Informatiche
Pavia, Italy
2 h-index
2 publications
34 citations
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6 publications · 219 citations
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5 publications · 589 citations
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3 publications · 57 citations
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3 publications · 79 citations
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3 publications · 171 citations
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