| Name | pgmpy JSON |
| Version |
0.1.26
JSON |
| download |
| home_page | https://github.com/pgmpy/pgmpy |
| Summary | A library for Probabilistic Graphical Models |
| upload_time | 2024-08-09 16:48:07 |
| maintainer | None |
| docs_url | None |
| author | Ankur Ankan |
| requires_python | None |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
|
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No coveralls.
|
<div align="center">
<img src="https://raw.githubusercontent.com/pgmpy/pgmpy/dev/logo/logo_color.png" width="300" height="300"/>
</div>
<div align="center">
[](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/)
</div>
<div align="center">
[](https://discord.gg/DRkdKaumBs)
[](https://pgmpy.org)
[](https://github.com/pgmpy/pgmpy/tree/dev/examples)
[](https://github.com/pgmpy/pgmpy_notebook)
</div>
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 both causal inference and probabilistic inference literatures to allow users to seamlessly work between both. 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) | <a target="_blank" href="https://colab.research.google.com/github/ankurankan/pgmpy/blob/dev/examples/Creating%20a%20Discrete%20Bayesian%20Network.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
- Structure Learning/Causal Discovery: [view](https://pgmpy.org/examples/Structure%20Learning%20in%20Bayesian%20Networks.html) | <a target="_blank" href="https://colab.research.google.com/github/ankurankan/pgmpy/blob/dev/examples/Structure%20Learning%20in%20Bayesian%20Networks.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
- Parameter Learning: [view](https://pgmpy.org/examples/Learning%20Parameters%20in%20Discrete%20Bayesian%20Networks.html) | <a target="_blank" href="https://colab.research.google.com/github/ankurankan/pgmpy/blob/dev/examples/Learning%20Parameters%20in%20Discrete%20Bayesian%20Networks.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
- Probabilistic Inference: [view](https://pgmpy.org/examples/Inference%20in%20Discrete%20Bayesian%20Networks.html) | <a target="_blank" href="https://colab.research.google.com/github/ankurankan/pgmpy/blob/dev/examples/Inference%20in%20Discrete%20Bayesian%20Networks.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
- Causal Inference: [view](https://pgmpy.org/examples/Causal%20Inference.html) | <a target="_blank" href="https://colab.research.google.com/github/https://pgmpy.org/examples/Causal%20Inference.html"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
- Extending pgmpy: [view](https://pgmpy.org/examples/Extending%20pgmpy.html) | <a target="_blank" href="https://colab.research.google.com/github/ankurankan/pgmpy/blob/dev/examples/Extending%20pgmpy.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>
<br/>
- 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, & Abinash Panda ( 2015 ). pgmpy: Probabilistic Graphical Models using Python . In Proceedings of the 14th Python in Science Conference (pp. 6 - 11 ).
```
Bibtex:
```
@InProceedings{ Ankan2015,
author = { {A}nkur {A}nkan and {A}binash {P}anda },
title = { pgmpy: {P}robabilistic {G}raphical {M}odels using {P}ython },
booktitle = { {P}roceedings of the 14th {P}ython in {S}cience {C}onference },
pages = { 6 - 11 },
year = { 2015 },
editor = { {K}athryn {H}uff and {J}ames {B}ergstra },
doi = { 10.25080/Majora-7b98e3ed-001 }
}
```
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 gitter 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)
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"description": "<div align=\"center\">\n <img src=\"https://raw.githubusercontent.com/pgmpy/pgmpy/dev/logo/logo_color.png\" width=\"300\" height=\"300\"/>\n</div>\n<div align=\"center\">\n\n\n[](https://pypistats.org/packages/pgmpy)\n[](https://pypi.org/project/pgmpy/)\n[](https://pypi.org/project/pgmpy/)\n[](https://github.com/pgmpy/pgmpy/blob/dev/LICENSE)\n[](http://pgmpy.org/pgmpy-benchmarks/)\n\n\n</div>\n\n<div align=\"center\">\n\n[](https://discord.gg/DRkdKaumBs)\n[](https://pgmpy.org)\n[](https://github.com/pgmpy/pgmpy/tree/dev/examples)\n[](https://github.com/pgmpy/pgmpy_notebook)\n\n</div>\n\npgmpy 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 both causal inference and probabilistic inference literatures to allow users to seamlessly work between both. It implements algorithms for structure learning/causal discovery, parameter estimation, probabilistic and causal inference, and simulations.\n\n- **Documentation:** https://pgmpy.org/\n- **Installation:** https://pgmpy.org/started/install.html\n- **Mailing List:** https://groups.google.com/forum/#!forum/pgmpy .\n- **Community chat:** [discord](https://discord.gg/DRkdKaumBs) (Older chat at: [gitter](https://gitter.im/pgmpy/pgmpy))\n\n\nExamples\n--------\n- Creating a Bayesian Network: [view](https://pgmpy.org/examples/Creating%20a%20Discrete%20Bayesian%20Network.html) | <a target=\"_blank\" href=\"https://colab.research.google.com/github/ankurankan/pgmpy/blob/dev/examples/Creating%20a%20Discrete%20Bayesian%20Network.ipynb\"> <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/> </a>\n- Structure Learning/Causal Discovery: [view](https://pgmpy.org/examples/Structure%20Learning%20in%20Bayesian%20Networks.html) | <a target=\"_blank\" href=\"https://colab.research.google.com/github/ankurankan/pgmpy/blob/dev/examples/Structure%20Learning%20in%20Bayesian%20Networks.ipynb\"> <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/> </a>\n- Parameter Learning: [view](https://pgmpy.org/examples/Learning%20Parameters%20in%20Discrete%20Bayesian%20Networks.html) | <a target=\"_blank\" href=\"https://colab.research.google.com/github/ankurankan/pgmpy/blob/dev/examples/Learning%20Parameters%20in%20Discrete%20Bayesian%20Networks.ipynb\"> <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/> </a>\n- Probabilistic Inference: [view](https://pgmpy.org/examples/Inference%20in%20Discrete%20Bayesian%20Networks.html) | <a target=\"_blank\" href=\"https://colab.research.google.com/github/ankurankan/pgmpy/blob/dev/examples/Inference%20in%20Discrete%20Bayesian%20Networks.ipynb\"> <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/> </a>\n- Causal Inference: [view](https://pgmpy.org/examples/Causal%20Inference.html) | <a target=\"_blank\" href=\"https://colab.research.google.com/github/https://pgmpy.org/examples/Causal%20Inference.html\"> <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/> </a>\n- Extending pgmpy: [view](https://pgmpy.org/examples/Extending%20pgmpy.html) | <a target=\"_blank\" href=\"https://colab.research.google.com/github/ankurankan/pgmpy/blob/dev/examples/Extending%20pgmpy.ipynb\"> <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/> </a>\n\n<br/>\n\n- Full List of Examples: https://github.com/pgmpy/pgmpy/tree/dev/examples\n- Tutorials: https://github.com/pgmpy/pgmpy_notebook/\n\nCiting\n======\nIf you use `pgmpy` in your scientific work, please consider citing us:\n\n```\nAnkur Ankan, & Abinash Panda ( 2015 ). pgmpy: Probabilistic Graphical Models using Python . In Proceedings of the 14th Python in Science Conference (pp. 6 - 11 ).\n```\n\nBibtex:\n```\n@InProceedings{ Ankan2015,\n author = { {A}nkur {A}nkan and {A}binash {P}anda },\n title = { pgmpy: {P}robabilistic {G}raphical {M}odels using {P}ython },\n booktitle = { {P}roceedings of the 14th {P}ython in {S}cience {C}onference },\n pages = { 6 - 11 },\n year = { 2015 },\n editor = { {K}athryn {H}uff and {J}ames {B}ergstra },\n doi = { 10.25080/Majora-7b98e3ed-001 }\n}\n```\n\nDevelopment\n============\n\nCode\n----\nThe latest codebase is available in the `dev` branch of the repository.\n\nBuilding from Source\n--------------------\nTo install pgmpy from the source code:\n```\n$ git clone https://github.com/pgmpy/pgmpy\n$ cd pgmpy/\n$ pip install -r requirements.txt\n$ python setup.py install\n```\n\nTo run the tests, you can use pytest:\n```\n$ pytest -v pgmpy\n```\n\nIf you face any problems during installation let us know, via issues, mail or at our gitter channel.\n\nContributing\n------------\nPlease feel free to report any issues on GitHub: https://github.com/pgmpy/pgmpy/issues.\n\nBefore opening a pull request, please have a look at our [contributing guide](\nhttps://github.com/pgmpy/pgmpy/blob/dev/Contributing.md) If you face any\nproblems in pull request, feel free to ask them on the mailing list or gitter.\n\nIf you would like to implement any new features, please have a discussion about it before starting to work on it.\nIf you are looking for some ideas for projects, we a list of **mentored projects** available at: https://github.com/pgmpy/pgmpy/wiki/Mentored-Projects.\n\nBuilding Documentation\n----------------------\nWe use sphinx to build the documentation. 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