Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/pgmpy/pgmpy
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
https://github.com/pgmpy/pgmpy
bayesian-networks causal-discovery causal-inference directed-acyclic-graph inference probabilistic-graphical-models python simulations structure-learning
Last synced: 10 days ago
JSON representation
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
- Host: GitHub
- URL: https://github.com/pgmpy/pgmpy
- Owner: pgmpy
- License: mit
- Created: 2013-09-20T08:18:58.000Z (about 11 years ago)
- Default Branch: dev
- Last Pushed: 2024-05-18T09:43:24.000Z (6 months ago)
- Last Synced: 2024-05-18T15:53:28.801Z (6 months ago)
- Topics: bayesian-networks, causal-discovery, causal-inference, directed-acyclic-graph, inference, probabilistic-graphical-models, python, simulations, structure-learning
- Language: Python
- Homepage: https://pgmpy.org/
- Size: 9.47 MB
- Stars: 2,627
- Watchers: 74
- Forks: 691
- Open Issues: 276
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: Contributing.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Authors: AUTHORS.rst
Awesome Lists containing this project
- awesome-python-machine-learning - pgmpy - pgmpy is a python library for working with Probabilistic Graphical Models. (Uncategorized / Uncategorized)
- awesome-sciml - pgmpy/pgmpy: Python Library for learning (Structure and Parameter) and inference (Probabilistic and Causal) in Bayesian Networks.
- awesome-list - pgmpy - A python library for working with Probabilistic Graphical Models. (Linear Algebra / Statistics Toolkit / Statistical Toolkit)
- awesome-python-machine-learning-resources - GitHub - 24% open · ⏱️ 22.08.2022): (概率统计)
- StarryDivineSky - pgmpy/pgmpy
README
[](https://pypistats.org/packages/pgmpy)
[](https://pypi.org/project/pgmpy/)
[](https://pypi.org/project/pgmpy/)
[](https://github.com/pgmpy/pgmpy/blob/dev/LICENSE)
[](http://pgmpy.org/pgmpy-benchmarks/)
[](https://discord.gg/DRkdKaumBs)
[](https://pgmpy.org)
[](https://github.com/pgmpy/pgmpy/tree/dev/examples)
[](https://github.com/pgmpy/pgmpy_notebook)
pgmpy is a Python package for working with Bayesian Networks and related models such as Directed Acyclic Graphs, Dynamic Bayesian Networks, and Structural Equation Models. It combines features from causal inference and probabilistic inference literature to allow users to seamlessly work between them. It implements algorithms for structure learning, causal discovery, parameter estimation, probabilistic and causal inference, and simulations.
- **Documentation:** https://pgmpy.org/
- **Installation:** https://pgmpy.org/started/install.html
- **Mailing List:** https://groups.google.com/forum/#!forum/pgmpy .
- **Community chat:** [discord](https://discord.gg/DRkdKaumBs) (Older chat at: [gitter](https://gitter.im/pgmpy/pgmpy))
Examples
--------
- Creating a Bayesian Network: [view](https://pgmpy.org/examples/Creating%20a%20Discrete%20Bayesian%20Network.html) | 
- Structure Learning/Causal Discovery: [view](https://pgmpy.org/examples/Structure%20Learning%20in%20Bayesian%20Networks.html) |
- Parameter Learning: [view](https://pgmpy.org/examples/Learning%20Parameters%20in%20Discrete%20Bayesian%20Networks.html) |
- Probabilistic Inference: [view](https://pgmpy.org/examples/Inference%20in%20Discrete%20Bayesian%20Networks.html) |
- Causal Inference: [view](https://pgmpy.org/examples/Causal%20Inference.html) |
- Extending pgmpy: [view](https://pgmpy.org/examples/Extending%20pgmpy.html) |
- Full List of Examples: https://github.com/pgmpy/pgmpy/tree/dev/examples
- Tutorials: https://github.com/pgmpy/pgmpy_notebook/
Citing
======
If you use `pgmpy` in your scientific work, please consider citing us:
```
Ankur Ankan, & Johannes Textor (2024). pgmpy: A Python Toolkit for Bayesian Networks. Journal of Machine Learning Research, 25(265), 1–8.
```
Bibtex:
```
@article{Ankan2024,
author = {Ankur Ankan and Johannes Textor},
title = {pgmpy: A Python Toolkit for Bayesian Networks},
journal = {Journal of Machine Learning Research},
year = {2024},
volume = {25},
number = {265},
pages = {1--8},
url = {http://jmlr.org/papers/v25/23-0487.html}
}
```
Development
============
Code
----
The latest codebase is available in the `dev` branch of the repository.
Building from Source
--------------------
To install pgmpy from the source code:
```
$ git clone https://github.com/pgmpy/pgmpy
$ cd pgmpy/
$ pip install -r requirements.txt
$ python setup.py install
```
To run the tests, you can use pytest:
```
$ pytest -v pgmpy
```
If you face any problems during installation let us know, via issues, mail or at our discord channel.
Contributing
------------
Please feel free to report any issues on GitHub: https://github.com/pgmpy/pgmpy/issues.
Before opening a pull request, please have a look at our [contributing guide](
https://github.com/pgmpy/pgmpy/blob/dev/Contributing.md) If you face any
problems in pull request, feel free to ask them on the mailing list or gitter.
If you would like to implement any new features, please have a discussion about it before starting to work on it.
If you are looking for some ideas for projects, we a list of **mentored projects** available at: https://github.com/pgmpy/pgmpy/wiki/Mentored-Projects.
Building Documentation
----------------------
We use sphinx to build the documentation. Please refer: https://github.com/pgmpy/pgmpy/wiki/Maintenance-Guide#building-docs for steps to build docs locally.
License
=======
pgmpy is released under MIT License. You can read about our license at [here](https://github.com/pgmpy/pgmpy/blob/dev/LICENSE)