Guide

This guide is intended for an audience that has little to none experience with Gaussian processes, and a basic knowledge of Python and Numpy. It is recommended to read the sections in order. If you prefer bare code examples, look at the Example scripts chapter.

• 1. Installation
• 2. First example: a sine
• 3. More on kernels
• 4. Taking derivatives
• 5. Taking integrals
• 6. A custom kernel: text classification
• 7. Multidimensional input
• 8. Partial derivatives
• 9. Multidimensional output
• 10. Splitting components
• 11. Hyperparameters
• 12. Hyperparameters in the input
• 13. Nonlinear models
• 14. Optimization