loushuai / MLRaptor

Efficient online variational Bayesian inference algorithms for common machine learning tasks. Includes mixture models (like GMMs) and admixture models (like LDA). Implemented in Python.

MLRaptor : EM/Variational Inference for Exponential Family Graphical Models.
Website: http://michaelchughes.github.com/MLRaptor/
Author:  Mike Hughes (www.michaelchughes.com)
Please email all comments/questions to mike <AT> michaelchughes.com

The repository is organized as follows:  
  expfam/ Defines python module for learning exp. fam. graphical models.

  doc/  contains human-readable documentation.

  data/ example dataset modules for loading/using toy data
      
Look for additional documentation and occasional updates on github:
   https://github.com/michaelchughes/MLRaptor
     
References:
The canonical textbook is:
  * Pattern Recognition and Machine Learning (PRML), by Christopher Bishop