mogpe documentation!

This package implements a Mixtures of Gaussian Process Experts (MoGPE) model with a GP-based gating network. Inference exploits factorisation through sparse GPs and trains a variational lower bound stochastically. It also provides the building blocks for implementing other Mixtures of Gaussian Process Experts models. mogpe uses GPflow 2.2/TensorFlow 2.4+ for running computations, which allows fast execution on GPUs, and uses Python ≥ 3.8. It was originally created by Aidan Scannell.

Getting Started

To get started please see the Install instructions. Notes on using mogpe can be found in Usage and the examples directory and notebooks show how the model can be configured and trained. Details on the implementation can be found in What’s going on with this code?! and the mogpe API.