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
The Emukit-playground is a demo to illustrate different concepts in emulation an uncertainty quantification.
Bayesian optimization is a sequential decision making approach to find the optimum of objective functions that areexpensive to evaluate.
Use Emukit to build emulators in scenarios where data of different levels of accuracy are available. Use this models in decision loops.
Experimental design addresses the problem of how to collect data points (experiments) to better control certainsources of variance of a model.
Bayesian quadrature is an active learning method for the value of an integral given queries of the integrand on a finite and usually small amount of input locations.
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.