https://github.com/avoss84/bmm_mix
Python implementation for Bernoulli mixture model inference via EM + MCMC
https://github.com/avoss84/bmm_mix
Last synced: 10 months ago
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Python implementation for Bernoulli mixture model inference via EM + MCMC
- Host: GitHub
- URL: https://github.com/avoss84/bmm_mix
- Owner: AVoss84
- Created: 2019-12-16T22:26:40.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-11-10T23:25:33.000Z (over 1 year ago)
- Last Synced: 2025-06-03T01:43:25.054Z (about 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 1020 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
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Metadata Files:
- Readme: README.md
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README
# *Bernoulli Mixture Models (BMM)*
This repository provides tools in a Python package *bernmix* for the unsupervised analysis of multivariate Bernoulli data with known number of cluster/groups using BMMs. Python 3.8.*
## Maximum likelihood estimation
Shows how to fit the model using [Expectation-Maximizition (EM)](https://github.com/AVoss84/bmm_mix/blob/master/EM_for_BMM.ipynb) algorithm as outlined in *Bishop (2006): Pattern Recognition and Machine Learning*.
## Fully Bayesian estimation
Shows how to fit the model using [Gibbs sampling](https://github.com/AVoss84/bmm_mix/blob/master/Gibbs_for_BMM.ipynb) algorithm.
```
from bernmix.utils import bmm_utils as bmm
```
### Installing
```
pip install -r requirements.txt
```
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details