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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)

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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.

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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)

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