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.
Install / Use
/learn @linkerlin/MLRaptorREADME
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
