A Matlab toolbox for Gaussian process regression, classification and preference learning
If you use this software, please reference it as follows : Tristan Fauvel (2021) GP_toolbox, a Matlab toolbox for Gaussian process regression, classification and preference learning.
- Gaussian process regression
- Gaussian process classification with Laplace approximation or Expectation-Propagation
- Gaussian process preference learning using conditional preference kernel
- Approximate sampling from posterior GP :
- Finite dimensional stationary kernel approximation using Sparse-Spectrum approximation (Lazaro-Gredilla et al, 2010)
- Finite dimensional stationary kernel approximation using Hilbert-space methods (Solin et al, 2010)
- Weight-space approximate sampling (Lazaro-Gredilla et al, 2010) or decoupled-bases approximate sampling (Wilson et al, 2020)
- Simply add GP_toolbox to your Matlab path
- Please refer to
documentation.md. - Each /Examples subfolder includes detailed description about how to use the toolbox.
- You can use GP_toolbox with BO_toolbox for Bayesian optimization (https://github.com/TristanFauvel/BO_toolbox)
This software is distributed under the MIT License. Please refer to the file LICENCE.txt included for details.