Python toolkit for emulation and decision making under uncertainty
Accessible and built with reusable components
Independent of the modelling framework. Use it with MXNet, TensorFlow, GPy, etc.
Open Source under the Apache-2.0 license
Emukit-playground

Emukit-playground

The Emukit-playground is a demo to illustrate different concepts in emulation an uncertainty quantification.
Bayesian Optimization

Bayesian Optimization

Bayesian optimization is a sequential decision making approach to find the optimum of objective functions that areexpensive to evaluate.
Multi-fidelity emulation

Multi-fidelity emulation

Use Emukit to build emulators in scenarios where data of different levels of accuracy are available. Use this models in decision loops.
Experimental design

Experimental design

Experimental design addresses the problem of how to collect data points (experiments) to better control certainsources of variance of a model.
Bayesian Quadrature

Bayesian Quadrature

Bayesian quadrature (BQ) is a numerical integration method that works best on low-dimensional and expensive integration tasks. BQ can use active learning to query the integrand ...
Sensitivity Analysis

Sensitivity Analysis

Sensitivity analysis is the study of how the variations in the outputs of a system can be assigned to different sources of variation in its inputs.