StoPy

StoPy is a numerical software for stoch Stochastic modelling in Python, which includes also inverse problems using Bayesian updating.

Requirements

The code is optimised for Python (version 3.6) and depends on the following numerical libraries:

• NumPy (version 1.15.4) and SciPy (version 1.1.0) for scientific computing as well as on the
• Scikit-sparse (version 0.4.4) for Cholesky decomposition

Structure of the code

At the moment the code contains the examples presented in the paper in References, which is focused on Bayesian updating.

The structure of the code is following:

• general package contains all auxiliary functions used in the code,
• research_articles package contains the numerial examples used in the paper,
• uq package contains all code related to uncertainty quantification and Bayesian updating.

All files in research_articles can be run as a python script, i.e. the file name_of_file.py can be run using the following shell command

python3 name_of_file.py

License

This repository is distributed under an open MIT license. If you find the code and approach interesting, you are kindly asked to cite the papers in References.

References

The code is based on the following papers, where you can find more theoretical information.

• Jaroslav Vondřejc and Hermann G. Matthies: Accurate computation of conditional expectation for highly non-linear problems. 2018. arXiv:1806.03234

Particularly see the folder 'research_papers' with code used in the publication.