Demos

There are several demos exemplifying the use of pyGPs for various Gaussian process () tasks. We recommend to first go through Basic GP Regression which introduces the regression model. Basic regression is the most intuitive and simplest learning task feasable with . The other demos will then provide a general insight into more advanced functionalities of the package. You will also find the implementation of the demos in the source folder under pyGPs/Demo.

The Demos give some theoretical explanations. Further, it is useful to have a look at our documentation on Kernels & Means and Optimizers.

Regression

• Basic Regression
• Sparse Regression

Classification

• Basic Classification
• Sparse Classification
• Multi-class Classification

Some examples for real-world data

• K-fold Cross-Validation
• Regression on Mauna Loa data
• Regression on UCI Housing data
• Semi-supervised Learning with Graphs
• Graph Kernels