Sequential Model Selection
Sequential Model Selection is a methodological framework within sequential analysis that iteratively evaluates and selects among competing statistical models based on accumulating evidence, distinguishing itself by incorporating Bayesian updating to refine model probabilities as new data become available.
72
Publications
4.7K
Citations
224
Authors
109
Institutions
Publications per year
2017–2026
20
Parents
72
Online Model Selection Based on the Variational Bayes
Masa-aki Sato · Neural Computation · 2001 · 382 citations
1–5 of 72
224
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University of California, San Francisco, United States
2 publications · 475 citations
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1–5 of 224
109
Carnegie Mellon University
Pittsburgh, United States
2 h-index
4 publications
318 citations
University of Wisconsin–Madison
Madison, United States
2 h-index
4 publications
168 citations
University of Cambridge
Cambridge, United Kingdom
2 h-index
4 publications
342 citations
Helsinki Institute for Information Technology
Espoo, Finland
2 h-index
5 publications
146 citations

Queensland University of Technology
Brisbane, Australia
2 h-index
5 publications
191 citations
University at Buffalo, State University of New York
Buffalo, United States
2 h-index
3 publications
243 citations
University of Douala
Douala, Cameroon
2 h-index
6 publications
95 citations
Stanford University
Stanford, United States
2 h-index
4 publications
318 citations
1–8 of 109
Repository · 5 publications · 190 citations
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3 publications · 60 citations
3
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2 publications · 254 citations
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2 publications · 475 citations
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2 publications · 84 citations
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