PyBGMM: Bayesian inference for Gaussian mixture model

Overview

Bayesian inference for Gaussian mixture model to reduce over-clustering via the powered Chinese restaurant process (pCRP). We use collapsed Gibbs sampling for posterior inference.

Code Structure

|-- GMM # base class for Gaussian mixture model
    |---- IGMM  # base class for infinite Gaussian mixture model
        |------ CRPMM     ## traditional Chinese restaurant process (CRP) mixture model
        |------ PCRPMM    ## powered Chinese restaurant process (pCRP) mixture model

Documentation

What do we include:

• Chinese restaurant process mixture model (CRPMM)

• Powered Chinese restaurant process (pCRP) mixture model

Examples

Dependencies

1. See requirements.txt

Lincense

MIT

Citation

The repo is based on the following research articles:

• Lu, Jun, Meng Li, and David Dunson. "Reducing over-clustering via the powered Chinese restaurant process." arXiv preprint arXiv:1802.05392 (2018).

References

1. 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.
2. Murphy, Kevin P. "Conjugate Bayesian analysis of the Gaussian distribution." def 1.2σ2 (2007): 16.
3. Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
4. Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." Journal of Machine Learning Research 12.Oct (2011): 2825-2830.
5. Rasmussen, Carl Edward. "The infinite Gaussian mixture model." Advances in neural information processing systems. 2000.
6. Tadesse, Mahlet G., Naijun Sha, and Marina Vannucci. "Bayesian variable selection in clustering high-dimensional data." Journal of the American Statistical Association 100.470 (2005): 602-617.