ProbNum: Probabilistic Numerics in Python
Mathematical Software
2021-12-07 v1 Machine Learning
Numerical Analysis
Numerical Analysis
Abstract
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential equation simulation. PNMs naturally incorporate prior information about a problem and quantify uncertainty due to finite computational resources as well as stochastic input. In this paper, we present ProbNum: a Python library providing state-of-the-art probabilistic numerical solvers. ProbNum enables custom composition of PNMs for specific problem classes via a modular design as well as wrappers for off-the-shelf use. Tutorials, documentation, developer guides and benchmarks are available online at www.probnum.org.
Cite
@article{arxiv.2112.02100,
title = {ProbNum: Probabilistic Numerics in Python},
author = {Jonathan Wenger and Nicholas Krämer and Marvin Pförtner and Jonathan Schmidt and Nathanael Bosch and Nina Effenberger and Johannes Zenn and Alexandra Gessner and Toni Karvonen and François-Xavier Briol and Maren Mahsereci and Philipp Hennig},
journal= {arXiv preprint arXiv:2112.02100},
year = {2021}
}