# Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models

arXiv: https://arxiv.org/abs/2402.10229 · HTML: https://mlsanity.com/paper/2402.10229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Statistical guarantees for the EM algorithm: From population to sample-based analysis](https://mlsanity.com/paper/1408.2156/) (2014)
- [A Two-round Variant of EM for Gaussian Mixtures](https://mlsanity.com/paper/1301.3850/) (2013)
- [Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation](https://mlsanity.com/paper/1306.2035/) (2013)
- [Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures](https://mlsanity.com/paper/1406.2206/) (2014)
- [Improved Inference of Gaussian Mixture Copula Model for Clustering and Reproducibility Analysis using Automatic Differentiation](https://mlsanity.com/paper/2010.14359/) (2020)
- [Model-based Clustering with Sparse Covariance Matrices](https://mlsanity.com/paper/1711.07748/) (2017)

## Built on by (most cited first, 1 of 1)

- [SCPP: A Unified Python Library for Soft Clustering](https://mlsanity.com/paper/2607.19620/) (2026)

## Abstract (arXiv)

textttMixture-Models is an open-source Python library for fitting Gaussian Mixture Models (GMM) and their variants, such as Parsimonious GMMs, Mixture of Factor Analyzers, MClust models, Mixture of Student's t distributions, etc. It streamlines the implementation and analysis of these models using various first/second order optimization routines such as Gradient Descent and Newton-CG through automatic differentiation (AD) tools. This helps in extending these models to high-dimensional data, which is first of its kind among Python libraries. The library provides user-friendly model evaluation tools, such as BIC, AIC, and log-likelihood estimation. The source-code is licensed under MIT license and can be accessed at urlhttps://github.com/kasakh/Mixture-Models. The package is highly extensible, allowing users to incorporate new distributions and optimization techniques with ease. We conduct a large scale simulation to compare the performance of various gradient based approaches against Expectation Maximization on a wide range of settings and identify the corresponding best suited approach.