pyMEF: a Python library for mixtures of exponential families

pyMEF: a Python library for mixtures of exponential families
OFFICIAL WEBSITE  TUTAORIAL  DOWNLOAD
Description
pyMEF is a Python framework allowing to manipulate, learn, simplify and compare mixtures of exponential families. It is designed to ease the use of various exponential families in mixture models.
See also jMEF for a Java implementation of the same kind of library and libmef for a faster C implementation.What are exponential families?
An exponential family is a generic set of probability distributions that admit the following canonical distribution:
Exponential families are characterized by the log normalizer function F, and include the following wellknown distributions: Gaussian (generic, isotropic Gaussian, diagonal Gaussian, rectified Gaussian or Wald distributions, lognormal), Poisson, Bernoulli, binomial, multinomial, Laplacian, Gamma (incl. chisquared), Beta, exponential, Wishart, Dirichlet, Rayleigh, probability simplex, negative binomial distribution, Weibull, von Mises, Pareto distributions, skew logistic, etc.
Mixtures of exponential families provide a generic framework for handling Gaussian mixture models (GMMs also called MoGs for mixture of Gaussians), mixture of Poisson distributions, and Laplacian mixture models as well.
Tutorials
A generic tutorial on the exponential families and the simplification of mixture models have been made during the workshop Matrix Information Geometries.
More pyMEF specific tutorials are available here:
Basic manipulation of mixture modelsBibliography
 Olivier Schwander, Frank Nielsen, Simplification de modèles de mélange issus d’estimateur par noyau, GRETSI 2011
 Olivier Schwander and Frank Nielsen, pyMEF  A framework for Exponential Families in Python, in Proceedings of the 2011 IEEE Workshop on Statistical Signal Processing
 Vincent Garcia, Frank Nielsen, and Richard Nock, Levels of details for Gaussian mixture models, in Proceedings of the Asian Conference on Computer Vision, Xi’an, China, September 2009
 Frank Nielsen and Vincent Garcia, Statistical exponential families: A digest with flash cards, arXiV, http://arxiv.org/abs/0911.4863, November 2009
 Frank Nielsen and Richard Nock, Sided and symmetrized Bregman centroids, in IEEE Transactions on Information Theory, 2009, 55, 20482059
 Frank Nielsen, JeanDaniel Boissonnat and Richard Nock, On Bregman Voronoi diagrams, in ACMSIAM Symposium on Data Mining, 2007, 746755
 A. Banerjee, S. Merugu, I. Dhillon, and J. Ghosh, Clustering with Bregman divergences, in Journal of Machine Learning Research, 2005, 6, 234245
See also jMEF for a Java implementation of the same kind of library and libmef for a faster C implementation.