Numerical issues in maximum likelihood parameter estimation for GP interpolation
Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolation

    
    
                
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            Abstract:This article investigates the origin of numerical issues in maximum likelihood parameter estimation for Gaussian process (GP) interpolation and investigates simple but effective strategies for improving commonly used open-source software implementations. This work targets a basic problem but a host of studies, particularly in the literature of Bayesian optimization, rely on off-the-shelf GP implementations. For the conclusions of these studies to be reliable and reproducible, robust GP implementations are critical.
    

    
    
  
      Submission history From: Emmanuel Vazquez [view email]                  [v1]
        Sun, 24 Jan 2021 16:30:55 UTC (76 KB)
    [v2]
        Tue, 27 Jul 2021 19:31:13 UTC (97 KB)
