PyMix: The Python mixture package

The Python Mixture Package (PyMix) is a freely available Python library implementing algorithms and data structures for a wide variety of data mining applications with basic and extended mixture models.


  • Finite mixture models of discrete and continuous features
  • Wide range of available distributions (Normal, Exponential, Discrete, Dirichlet, Normal-Gamma, Uniform, HMM)
  • Bayesian mixture models
  • ML and MAP parameter estimation
  • Context-specific independence structure learning
  • Partially supervised parameter learning
  • Parameter estimation for pairwise constrained samples

For more downloading the most recent version, documentation and the Pymix mailing list refer to the Pymix home page.

For further information see the main website at or contact Benjamin Georgi ( This software is a result of or used in the following projects: ComplexDiseases, CSIMixtures.


Members: Benjamin Georgi, Benjamin Georgi.


Georgi et al.. PyMix - The Python mixture package - a tool for clustering of heterogeneous biological data. BMC Bioinformatics 2010, 11:9.

Georgi. Context-specific Independence Mixture Models for Cluster Analysis of Biological Data. Ph.D. Thesis, Freie Universität Berlin, Jun 2009.

Costa et al.. Constrained Mixture Estimation for Analysis and Robust Classification of Clinical Time Series. Bioinformatics 2009, 12:25, i6–14. (ISMB 2009).

Georgi et al.. Partially-supervised protein subclass discovery with simultaneous annotation of functional residues. BMC Struct Biol. 2009, 9:68.

Costa et al.. Inferring differentiation pathways from gene expression. Bioinformatics 2008, 24:13, i156–164.

Georgi et al.. Partially-supervised context-specific independence mixture modeling. In workshop on Data Mining in Functional Genomics and Proteomics, ECML 2007, 2007.

Georgi et al.. Mixture model based group inference in fused genotype and phenotype data. In Studies in Classification, Data Analysis, and Knowledge Organization, Springer, 2007.

Georgi et al.. Context-Specific Independence Mixture Modelling for Protein Families. In Knowledge Discovery in Databases: PKDD 2007, Springer Berlin / Heidelberg, Volume 4702/2007, 79–90, 2007.

Georgi et al.. Context-specific independence mixture modeling for positional weight matrices. Bioinformatics 2006, 22:14, e166–e173.

Georgi. Mixture Modeling and Group Inference in Fused Genotype and Phenotype Data. Master's Thesis, Freie Universität Berlin, 2005.