USER GUIDE¶
Introductory theory¶
Note
#TODO Introduced some of the used basic terms and notations here to prepare for the detailed descriptions of each part.
Overview¶
This package is split into multiple topics corresponding to the folder structure of the package.

The folder surrogate_models contains all the functions and classes that are necessary in order to create and train the surrogate model. This includes
defining the input marginals
setting properties of the sampling in an experimental design
choosing the surrogate model and its properties
training the surrogate model on model evaluations in a straightforward or iterative manner
The computational model is linked via a pylink interface. We split this into the aspects Priors, input space and experimental design and Training surrogate models to provide insight into the options available in bayesvalidrox.
Postprocessing, Bayesian inference and Bayesian model comparison can be applied to trained surrogate models, or using the underlying models themselves.