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.
- Gaussian Process Demo (Python)
- Multi-Output Tutorial
- Gaussian Process Kernel Demos (Python)
- Mutual Information for Computer Experiments (MICE) Demos
- History Matching Demos
- Kernel Dimension Reduction (KDR) Demos
- Gaussian Process Demo (GPU)
- Gaussian Process Demo (R)
- Gaussian Process Demo with Small Sample Size