This is a collection of some of the python programs surrounding the usage of Gaussian process emulators in Bayesian inverse problems as part of my MSc thesis at the University of Bonn.
As of May 13, 2019, it includes: (Q)MC and MCMC algorithms, finite element methods, various covariance matrices, Gaussian processes and Gaussian process regression, various visualisations, Bayesian inference and approximate Bayesian inference with Gaussian process emulators, localised Lagrange bases preconditioners. Most of them are based on numpy, scipy and matplotlib. Recent extensions include preconditioning with localised bases for kernel spaces.
I intend to follow the PEP8 style guide. For most of the modules, I include unittests.
Please let me know about any bugs, issues or inefficiencies.
Test coverage: fairly low
All rights reserved, Nicholas Krämer, 2019