pgmpy is a Python library for probabilistic graphical models such as Bayesian Networks and Markov Networks. It provides tools for learning, inference, and probabilistic reasoning used in machine learning and AI research.
How It Differs
While many Python machine learning libraries focus on deep learning or traditional ML algorithms, pgmpy focuses specifically on probabilistic graphical models and Bayesian networks, enabling reasoning under uncertainty and causal modeling.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.
## 📋 Pull Request Information
**Original PR:** https://github.com/vinta/awesome-python/pull/2962
**Author:** [@Ashi-tha](https://github.com/Ashi-tha)
**Created:** 3/15/2026
**Status:** ✅ Merged
**Merged:** 3/16/2026
**Merged by:** [@JinyangWang27](https://github.com/JinyangWang27)
**Base:** `master` ← **Head:** `add-pgmpy-library`
---
### 📝 Commits (1)
- [`9eb21b2`](https://github.com/vinta/awesome-python/commit/9eb21b268e8c4558dd134964f124c1d0529e1e14) Add pgmpy library to Machine Learning section
### 📊 Changes
**1 file changed** (+1 additions, -0 deletions)
<details>
<summary>View changed files</summary>
📝 `README.md` (+1 -0)
</details>
### 📄 Description
## Project
[pgmpy](https://github.com/pgmpy/pgmpy)
## Checklist
- [x] One project per PR
- [x] PR title format: `Add project-name`
- [x] Entry format: `* [project-name](url) - Description ending with period.`
- [x] Description is concise and short
## Why This Project Is Awesome
Which criterion does it meet? (pick one)
- [ ] **Industry Standard** - The go-to tool for a specific use case
- [ ] **Rising Star** - 5000+ stars in < 2 years, significant adoption
- [x] **Hidden Gem** - Exceptional quality, solves niche problems elegantly
Explain:
pgmpy is a Python library for probabilistic graphical models such as Bayesian Networks and Markov Networks. It provides tools for learning, inference, and probabilistic reasoning used in machine learning and AI research.
## How It Differs
While many Python machine learning libraries focus on deep learning or traditional ML algorithms, pgmpy focuses specifically on probabilistic graphical models and Bayesian networks, enabling reasoning under uncertainty and causal modeling.
---
<sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
Blocking a user prevents them from interacting with repositories, such as opening or commenting on pull requests or issues. Learn more about blocking a user.
📋 Pull Request Information
Original PR: https://github.com/vinta/awesome-python/pull/2962
Author: @Ashi-tha
Created: 3/15/2026
Status: ✅ Merged
Merged: 3/16/2026
Merged by: @JinyangWang27
Base:
master← Head:add-pgmpy-library📝 Commits (1)
9eb21b2Add pgmpy library to Machine Learning section📊 Changes
1 file changed (+1 additions, -0 deletions)
View changed files
📝
README.md(+1 -0)📄 Description
Project
pgmpy
Checklist
Add project-name* [project-name](url) - Description ending with period.Why This Project Is Awesome
Which criterion does it meet? (pick one)
Explain:
pgmpy is a Python library for probabilistic graphical models such as Bayesian Networks and Markov Networks. It provides tools for learning, inference, and probabilistic reasoning used in machine learning and AI research.
How It Differs
While many Python machine learning libraries focus on deep learning or traditional ML algorithms, pgmpy focuses specifically on probabilistic graphical models and Bayesian networks, enabling reasoning under uncertainty and causal modeling.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.