Metadata-Version: 2.1
Name: studenttmixture
Version: 0.0.2
Summary: Mixture modeling algorithms using the Student's t-distribution
Home-page: https://github.com/jlparki/mix_T
Author: Jonathan Parkinson
Author-email: jlparkinson1@gmail.com
License: MIT
Description: # 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. You can select the number of components
        using either prior knowledge or the information criteria calculated by the model
        (e.g. AIC, BIC).
        2) Modeling / clustering a dataset using a mixture of multivariate Student's 
        t distributions fit via the variational mean-field approximation. Depending on the
        hyperparameters you select, the fitting process will automatically "choose" an 
        appropriate number of clusters, so the number of components in this case acts
        as an upper bound.
        3) Modeling / clustering an infinite mixture of Student's t-distributions (i.e. a Dirichlet process). In practice,
        this model is fitted using some small modifications to the mean-field recipe and has
        some of the same advantages and limitations.
        
        (1) and (2) are currently available; (3) will be available in version 0.0.3.
        
        Unittests for the package are in the tests folder.
        
        ### Installation
        
            pip install studenttmixture
        
        ### Usage
        
        - [EMStudentMixture](https://github.com/jlparkI/mix_T/blob/main/Documentation/Finite_Mixture_Docs.md)<br>
        - [VariationalStudentMixture](https://github.com/jlparkI/mix_T/blob/main/Documentation/Variational_Mixture_Docs.md)<br>
        - [Tutorial: Modeling with mixtures of t-distributions](https://github.com/jlparkI/mix_T/blob/main/Documentation/Tutorial.md)<br>
        
        
        ### Background
        
        - [Deriving the mean-field formula](https://github.com/jlparkI/mix_T/blob/main/Documentation/variational_mean_field.pdf)<br>
        
        ### Upcoming in future versions
        
        - [Planned for version 0.0.3](https://github.com/jlparkI/mix_T/blob/main/Documentation/planned_mods.md)
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
