ProbNum: Probabilistic Numerics in Python

2021/12/03 by Jonathan Wenger, Wenger, Jonathan, Nicholas Krämer +21 · 1 citation
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Mathematical Software (cs.MS) #Numerical Analysis (math.NA) #Statistics Education and Methodologies

paper · pdf · doi:10.48550/arxiv.2112.02100

openalex publication_date 2021/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• rodeo: Probabilistic Methods of Parameter Inference for Ordinary Differential Equations

Related

• Probabilistic Programming in Python using PyMC
• ProSper -- A Python Library for Probabilistic Sparse Coding with\n Non-Standard Priors and Superpositions
• Pomegranate: fast and flexible probabilistic modeling in python
• QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models
• SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks