Gaussian Process Model Selection
Gaussian Process Model Selection is the systematic process of identifying the optimal Gaussian process model among a set of candidate models by evaluating their structural assumptions, covariance functions, and hyperparameters relative to observed data and prescribed criteria such as predictive accuracy or generalization performance.
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5K
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218
Authors
124
Institutions
Publications per year
2017–2026
38
Parents
70
Approximations for Binary Gaussian Process Classification
Hannes Nickisch, Carl Edward Rasmussen · 2008 · 305 citations
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Texas A&M University
College Station, United States
5 h-index
13 publications
312 citations

Institut de Mathématiques de Toulouse
Toulouse, France
2 h-index
3 publications
343 citations
Max Planck Society
Munich, Germany
2 h-index
3 publications
621 citations

University of Edinburgh
Edinburgh, United Kingdom
2 h-index
5 publications
254 citations
Helsinki Institute for Information Technology
Espoo, Finland
2 h-index
6 publications
292 citations
Simon Fraser University
Burnaby, Canada
2 h-index
5 publications
388 citations
Commissariat à l'Énergie Atomique et aux Énergies Alternatives
Paris, France
2 h-index
3 publications
635 citations
Université Paris-Sud
Orsay, France
2 h-index
3 publications
2.5K citations
1–8 of 124
3 publications · 206 citations
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3 publications · 54 citations
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2 publications · 92 citations
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2 publications · 49 citations
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2 publications · 327 citations
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