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.

70

Publications

5K

Citations

218

Authors

124

Institutions

Publications per year

2017–2026

38

Authors

218

  1. AV
    Aki Vehtari

    Aalto University, Finland

    3 publications · 104 citations

    3

    h-index

  2. BI
    Bertrand Iooss

    Institut de Mathématiques de Toulouse, France

    2 publications · 326 citations

    2

    h-index

  3. JK
    Juš Kocijan

    Jožef Stefan Institute, Slovenia

    2 publications · 241 citations

    2

    h-index

  4. LL
    Lester Lik Teck Chan

    Chung Yuan Christian University, Taiwan

    2 publications · 41 citations

    2

    h-index

  5. PM
    Pascal Massart

    Université Paris-Sud, France

    2 publications · 1.4K citations

    2

    h-index

1–5 of 218

Venues

  1. Technometrics

    3 publications · 206 citations

    3

    h-index

  2. Expert Systems with Applications

    3 publications · 54 citations

    3

    h-index

  3. Journal of Hydrology

    2 publications · 92 citations

    2

    h-index

  4. Neurocomputing

    2 publications · 49 citations

    2

    h-index

  5. Reliability Engineering & System Safety

    2 publications · 327 citations

    2

    h-index

1–5