Metadata-Version: 2.1
Name: probnum
Version: 0.1.8
Summary: Probabilistic Numerics in Python.
Home-page: https://github.com/probabilistic-numerics/probnum
Author: ProbNum Authors
Author-email: 
License: MIT
Description: # <a href="https://probnum.readthedocs.io"><img align="left" src="https://raw.githubusercontent.com/probabilistic-numerics/probnum/master/docs/source/img/pn_logo.png" alt="probabilistic numerics" width="64" style="padding-right: 10px; padding left: 10px;" title="Probabilistic Numerics in Python"/></a> ProbNum
        [![CI build](https://img.shields.io/github/workflow/status/probabilistic-numerics/probnum/Linting?logo=github&logoColor=white&label=CI-build)](https://github.com/probabilistic-numerics/probnum/actions?query=workflow%3ACI-build)
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        ---
        
        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.
        These uncertainties can be used in downstream decisions.
        
        Currently, available solvers are:
        
        - **Linear solvers:** Solve *Ax = b* for *x*.
        - **ODE solvers:** Solve *&#7823;(t) = f(&#8201;y(t), t&#8201;)* for *y*.
        - **Integral solvers (quadrature):** Solve *F = &#x222b; f(x)&#8201;p(x)&#8201;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.
        
        The research field underpinning the ProbNum library is called 
        `probabilistic numerics <http://probabilistic-numerics.org/>`_ (PN) 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`.
        ```bash
        pip install probnum
        ```
        Alternatively, you can install the latest version from source.
        ```bash
        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**](https://probnum.readthedocs.io). It contains a 
        [quickstart guide](https://probnum.readthedocs.io/en/latest/introduction/quickstart.html) 
        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](https://probnum.readthedocs.io/en/latest/development/contributing.html) before
        making a pull request.
        
        A list of core contributors to ProbNum can be found
        [here](https://probnum.readthedocs.io/en/latest/development/code_contributors.html).
        
        ## License and Contact
        This work is released under the [MIT License](https://github.com/probabilistic-numerics/probnum/blob/master/LICENSE.txt).
        
        Please submit an [issue on GitHub](https://github.com/probabilistic-numerics/probnum/issues/new) to report bugs or
        request changes.
        
Keywords: probabilistic-numerics,machine-learning,numerical-methods
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: jax
Provides-Extra: zoo
Provides-Extra: full
Provides-Extra: test
