Metadata-Version: 2.4
Name: studenttmixture
Version: 1.11
Summary: Mixture modeling algorithms using the Student's t-distribution
Author-email: Jonathan Parkinson <jlparkinson1@gmail.com>
License: Copyright (c) 2018 The Python Packaging Authority
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Project-URL: Homepage, https://github.com/jlparkI/mix_T
Project-URL: Documentation, https://github.com/jlparkI/mix_T
Keywords: mixture models,T distribution,student's T distribution,mixture of T distributions,mixture of Student's T distributions,variational mixture,variational inference
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-learn
Dynamic: license-file

# 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)<br>
- [VariationalStudentMixture](https://github.com/jlparkI/mix_T/blob/main/docs/Variational_Mixture_Docs.md)<br>
- [Tutorial: Modeling with mixtures](https://github.com/jlparkI/mix_T/blob/main/docs/Tutorial.md)<br>

### Background

- [Deriving the mean-field formula](https://github.com/jlparkI/mix_T/blob/main/docs/variational_mean_field.pdf)<br>
