https://github.com/jlparki/mix_t
Python (pip) package for fitting mixtures of Student's t-distributions using either maximum likelihood (EM) or Bayesian methodology (variational mean-field)
https://github.com/jlparki/mix_t
mixture-model student-t
Last synced: 2 months ago
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Python (pip) package for fitting mixtures of Student's t-distributions using either maximum likelihood (EM) or Bayesian methodology (variational mean-field)
- Host: GitHub
- URL: https://github.com/jlparki/mix_t
- Owner: jlparkI
- License: mit
- Created: 2020-12-15T03:56:28.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2025-09-23T20:52:12.000Z (9 months ago)
- Last Synced: 2025-12-15T14:36:52.121Z (6 months ago)
- Topics: mixture-model, student-t
- Language: Python
- Homepage:
- Size: 1.29 MB
- Stars: 10
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# studenttmixture
Mixtures of multivariate Student's t distributions are widely used for clustering
data that may contain outliers, but scipy and scikit-learn do not at present
offer classes for fitting Student's t mixture models. This package provides classes
for:
1) Modeling / clustering a dataset using a finite mixture of multivariate Student's
t distributions fit via the EM algorithm. This is analogous to scikit-learn's
GaussianMixture.
2) Modeling / clustering a dataset using a mixture of multivariate Student's
t distributions fit via the variational mean-field approximation. This is analogous to
scikit-learn's BayesianGaussianMixture.
### Installation
pip install studenttmixture
Starting with version 1.11, this is a pure Python package so installation
should be very straightforward.
Dependencies are numpy, scipy and scikit-learn.
### Usage
- [EMStudentMixture](https://github.com/jlparkI/mix_T/blob/main/docs/Finite_Mixture_Docs.md)
- [VariationalStudentMixture](https://github.com/jlparkI/mix_T/blob/main/docs/Variational_Mixture_Docs.md)
- [Tutorial: Modeling with mixtures](https://github.com/jlparkI/mix_T/blob/main/docs/Tutorial.md)
### Background
- [Deriving the mean-field formula](https://github.com/jlparkI/mix_T/blob/main/docs/variational_mean_field.pdf)