Bayesian Principal Component Analysis
Bayesian Principal Component Analysis is a probabilistic dimensionality reduction method that extends classical principal component analysis by incorporating a Bayesian framework to estimate principal components through posterior inference over model parameters, thereby enabling uncertainty quantification and automatic rank determination.
84
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5.1K
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298
Authors
167
Institutions
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2017–2026
23
Variants
Bpca
84
A Bayesian missing value estimation method for gene expression profile data
Shigeyuki Oba, Masa-aki Sato, Ichiro Takemasa et al. · Bioinformatics · 2003 · 772 citations · Full text
Long Chen, Xinlei Zhao · Review of Financial Studies · 2009 · 284 citations
Genetic diversity and subdivision of 57 European and Middle‐Eastern sheep breeds
Michael W. Bruford, Trinidad Pérez, S. Dalamitra et al. · Animal Genetics · 2007 · 242 citations
Wesley K. Thompson, Deanna M. Barch, James M. Bjork et al. · Developmental Cognitive Neuroscience · 2018 · 233 citations · Full text
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167

University of Alberta
Edmonton, Canada
4 h-index
7 publications
439 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
5 publications
511 citations
Tongji University
Shanghai, China
3 h-index
9 publications
220 citations

University of Minnesota
Minneapolis, United States
3 h-index
4 publications
276 citations

Universidad de La Laguna
San Cristóbal de La Laguna, Spain
2 h-index
2 publications
32 citations
Duke University
Durham, United States
2 h-index
5 publications
891 citations
University College London
London, United Kingdom
2 h-index
4 publications
130 citations
East China University of Science and Technology
Shanghai, China
2 h-index
2 publications
115 citations
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CSDA · 2 publications · 58 citations
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2 publications · 224 citations
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ApJ · 2 publications · 81 citations
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1 publications · 41 citations
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1 publications · 159 citations
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