  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.
