Welcome to Multi-Output GP Emulator’s documentation!

mogp_emulator is a Python package for fitting Gaussian Process Emulators to computer simulation results. The code contains routines for fitting GP emulators to simulation results with a single or multiple target values, optimizing hyperparameter values, and making predictions on unseen data. The library also implements experimental design, dimension reduction, and calibration tools to enable modellers to understand complex computer simulations.

The following pages give a brief overview of the package, instructions for installation, and an end-to-end tutorial describing a Uncertainty Quantification workflow using mogp_emulator. Further pages outline some additional examples, more background details on the methods in the MUCM Toolkit, full implementation details, and some included benchmarks.

Introduction and Installation:

For a more detailed description of some of the Uncertainty Quantification methods used in this package, see the MUCM toolkit pages:

Detailed information on all implemented classes and functions are described in the following pages:

For some more speicifs on benchmarks involving the implementation, see the following benchmark examples:

Indices and tables