Additive Kernels for Gaussian Process Modeling

Understand

Gaussian Process (GP) models are often used as mathematical approximations of computationally expensive experiments.

• Provided that its kernel is suitably chosen and that enough data is available to obtain a reasonable fit of the simulator, a GP model can beneficially be used for tasks such as prediction, optimization, or Monte-Carlo-based quantification of uncertainty.
• However, the former conditions become unrealistic when using classical GPs as the dimension of input increases.
• One popular alternative is then to turn to Generalized Additive Models (GAMs), relying on the assumption that the simulator's response can approximately be decomposed as a sum of univariate functions.