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.

Folder structure of **bayesvalidrox**

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.