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readme.rst

Bayes GMM: Bayesian Gaussian Mixture Models

Overview

Both the finite Bayesian Gaussian mixture model (FBGMM) and infinite Gaussian mixture model (IGMM) are implemented using collapsed Gibbs sampling.

Examples and testing code

  • Run make test to run unit tests.
  • Run make test_coverage to check test coverage.
  • Look at the examples in the examples/ directory.

Dependencies

References and notes

If you use this code, please cite:

  • H. Kamper, A. Jansen, S. King, and S. Goldwater, "Unsupervised lexical clustering of speech segments using fixed-dimensional acoustic embeddings", in Proceedings of the IEEE Spoken Language Technology Workshop (SLT), 2014.

In the code, references are made to the following:

  • K. P. Murphy, "Conjugate Bayesian analysis of the Gaussian distribution," 2007, [Online]. Available: http://www.cs.ubc.ca/~murphyk/mypapers.html
  • K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA: MIT Press, 2012.
  • F. Wood and M. J. Black, "A nonparametric Bayesian alternative to spike sorting," J. Neurosci. Methods, vol. 173, no. 1, pp. 1-12, 2012.

Some notes on the mathematical details can also be found at:

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Bayesian Gaussian mixture models in Python.

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