# Gausian process regressionΒΆ

SMT implements several surrogate models related to Gaussian process regression:

KRG implements classic gaussian process regression.

KPLS and KPLSK are variants using PLS dimension reduction to address high-dimensional training data.

GPX is a re-implementation of KRG and KPLS using Rust for faster training/prediction operations.

GEKPLS leverages on derivatives training data to improve the surrogate model quality.

MGP takes into account the uncertainty of the hyperparameters defined as a density function.

SGP implements sparse methods allowing to deal with large training dataset as others implementations have a time complexity of \(O(n^3)\) as well as a \(O(n^2)\) memory cost in the number \(n\) of training points.

Here below, the links to the dedicated pages: