Metadata-Version: 2.4
Name: compiled-knowledge
Version: 4.1.0a3
Summary: A Python package for compiling and querying discrete probabilistic graphical models.
Author-email: Barry Drake <barry@compiledknowledge.org>
License-Expression: MIT
Project-URL: Homepage, https://github.com/ropeless/compiled_knowledge
Project-URL: Issues, https://github.com/ropeless/compiled_knowledge/issues
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: llvmlite
Requires-Dist: numpy
Requires-Dist: scipy
Dynamic: license-file

Compiled Knowledge
==================

Compiled Knowledge is a Python package for compiling and querying discrete probabilistic graphical models.
The aim of the project is:
  - to provide a Python library for compiling and querying
    probabilistic graphical models, specifically discrete factor graphs,
    which includes Bayesian networks
  - to be extremely efficient, flexible, and easy to use
  - to exhibit excellent design, code, and documentation
  - to support researchers and businesses wanting to explore and use 
    probabilistic artificial intelligence.

License
=======

MIT license (see the file `LICENSE.txt`).


More Information
================

Refer to the project online documentation at
[compiled-knowledge.readthedocs.io](https://compiled-knowledge.readthedocs.io/).

The primary repository for the project is 
[github.com/ropeless/compiled_knowledge](https://github.com/ropeless/compiled_knowledge). 

The Python package is available on PyPI, see
[pypi.org/project/compiled-knowledge](https://pypi.org/project/compiled-knowledge/).

For more information email
[info@compiledknowledge.org](mailto:info@compiledknowledge.org).
