GitHub - linkerlin/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.

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

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Name		Name	Last commit message	Last commit date
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.deprecated		.deprecated
data		data
doc		doc
expfam		expfam
scripts		scripts
util		util
viz		viz
.gitignore		.gitignore
LearnExpFam.py		LearnExpFam.py
README		README
TODO		TODO

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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

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

machine-learning graphical

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