Example GPy use¶
This set of examples shows some of the functionality of the GPy Gaussian process framework in python. The framework is BSD licensed and we welcome collaborators to develop new functionality.
Multiple Output Gaussian Processes¶
- Coregionalization with Gaussian Processes This tutorial shows the use of a coregionalized model within GPy. In particular such models can be used for multi-task or multi-output learning.
- [Coregionalization on Marathon Data](./multiple outputs.ipynb) This tutorial runs the multioutput regression on a higher level, introducing stacked hierarchical multitask regression.
Different Noise Models¶
- GP classification A very simple turorial on GP classification.
- [Count Data with GPy](./Poisson regression tutorial.ipynb) This tutorial gives an example of Poisson regression using GPy.
- Heteroschedastic Gaussian Processes This tutorial shows how heteroschedastic Gaussian processes can be fit using GPy (with an interactive widget!).
Approximations¶
- Sparse Gaussian Processes This tutorial gives a quick overview of the variational approximation used to fit sparse Gaussian processes.
SVI¶
Partially parametric models¶
MCMC¶
Other apllications¶