GPflow manual#

You can use this document to get familiar with GPflow. We’ve split up the material into four different categories: basics, understanding, advanced needs, and tailored models. We have also provided a flow diagram to guide you to the relevant parts of GPflow for your specific problem.

Basics#

This section covers the elementary uses of GPflow, and shows you how to use GPflow for your basic datasets with existing models.

In each notebook we go over the data format, model setup, model optimization, and prediction options.

Understanding#

This section covers the building blocks of GPflow from an implementation perspective, and shows how the different modules interact as a whole.

Advanced needs#

This section explains the more complex models and features that are available in GPflow.

Models#

Features#

  • Natural gradients: how to optimize the variational approximate posterior’s parameters.

  • Monitoring Optimisation: how to monitor the model during optimisation: running custom callbacks and writing images and model parameters to TensorBoards.

Tailored models#

This section shows how to use GPflow’s utilities and codebase to build new probabilistic models. These can be seen as complete examples.

Theoretical notes#

The following notebooks relate to the theory of Gaussian processes and approximations. These are not required reading for using GPflow, but are included for those interested in the theoretical underpinning and technical details.