Skip to main content

Probabilistic Numerics in Python.

Project description

probabilistic numerics

Learn to Approximate. Approximate to Learn.

Probabilistic Numerics in Python.



ProbNum is a Python toolkit for solving numerical problems in linear algebra, optimization, quadrature and differential equations. ProbNum solvers not only estimate the solution of the numerical problem, but also its uncertainty (numerical error) which arises from finite computational resources, discretization and stochastic input. This numerical uncertainty can be used in downstream decisions.

Currently, available solvers are:

  • Linear solvers: Solve Ax = b for x.
  • ODE solvers: Solve ẏ(t) = f( y(t), t ) for y.
  • Integral solvers (quadrature): Solve F = ∫ f(x) p(x) dx for F.

Lower level structure includes:

  • Random variables and random processes, as well as arithmetic operations thereof.
  • Memory-efficient and lazy implementation of linear operators.
  • Filtering and smoothing for (probabilistic) state-space models, mostly variants of Kalman filters.

ProbNum is underpinned by the research field probabilistic numerics (PN), which lies at the intersection of machine learning and numerics. PN aims to quantify uncertainty arising from intractable or incomplete numerical computation and from stochastic input using the tools of probability theory. The general vision of probabilistic numerics is to provide well-calibrated probability measures over the output of a numerical routine, which then can be propagated along the chain of computation.

Installation

To get started install ProbNum using pip.

pip install probnum

Alternatively, you can install the latest version from source.

pip install git+https://github.com/probabilistic-numerics/probnum.git

Note: This package is currently work in progress, therefore interfaces are subject to change.

Documentation and Examples

For tips on getting started and how to use this package please refer to the documentation. It contains a quickstart guide and Jupyter notebooks illustrating the basic usage of the ProbNum solvers.

Package Development

This repository is currently under development and benefits from contribution to the code, examples or documentation. Please refer to the contribution guidelines before making a pull request.

A list of core contributors to ProbNum can be found here.

License and Contact

This work is released under the MIT License.

Please submit an issue on GitHub to report bugs or request changes.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for probnum, version 0.1.9
Filename, size File type Python version Upload date Hashes
Filename, size probnum-0.1.9-py2.py3-none-any.whl (189.9 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size probnum-0.1.9.tar.gz (59.1 MB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page