ProbNum-Evaluation

Evaluate the performance of (probabilistic) numerical methods. What makes an efficient and well calibrated probabilistic numerical algorithm? We don’t know either, but at least we can offer some quantification.

ProbNum-Evaluation offers a range of evaluation measures, including error analyses (with respect to different norms), calibration measures (chi-square statistics, non-credibility indices) and more.

To learn how to use ProbNum-Evaluation check out the quickstart guide and the tutorials. For guidelines how to contribute to ProbNum-Evaluation, please refer to the ` ProbNum documentation <https://probnum.readthedocs.io/en/latest/development/contributing.html>`.

API Documentation

• probnumeval.timeseries
• probnumeval.multivariate
• probnumeval.visual
• probnumeval.config

Tutorials

Quantify approximation error and calibration of a PN Method

Animate samples from a Gaussian distribution

Animate Samples from a Gauss-Markov Posterior

Other

• GitHub Repository
• License

Indices

• Index

• Module Index