More coming...
Approximation inference (Bayesian inference) for finite Gaussian mixture model (FGMM) and infinte Gaussian mixture model (IGMM) includes variational inference and Monte Carlo methods. Here we only use Monte Carlo methods. In particular, we use collapsed Gibbs sampling to do the inference.
|-- GMM # base class for Gaussian mixture model
|---- FGMM # base class for finite Gaussian mixture model
|------ PFGMM
|------ CSFGMM
|------ LSFGMM
|---- IGMM # base class for infinite Gaussian mixture model
|------ CRPMM
|------ PCRPMM ## powered Chinese restaurant process (pCRP) mixture model
|------ CSIGMM
|------ LSIGMM
|------ SubCRPMM ## Sub-clustering with CRP mixture model for high-dimensional data
What do we include:
-
Finite Gaussian mixture model
-
Hyperprior on Dirichlet distribution (for finite Gaussian mixture model)
-
Chinese restaurant process mixture model (CRPMM)
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Powered Chinese restaurant process (pCRP) mixture model
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Adaptive powered Chinese restaurant process (Ada-pCRP) mixture model
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Constrained sampling for Chinese restaurant process mixture model
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Bayesian variable selection in Chinese restaurant process mixture (Sub-CRP)
What we will include:
- Hyperprior on Dirichlet process prior (for infinite Gaussian mixture model)
Code | Description |
---|---|
CRPMM 1d | Chinese restaurant process mixture model for 1d data |
CRPMM 2d | Chinese restaurant process mixture model for 2d data |
pCRPMM 1d | powered Chinese restaurant process mixture model for 1d data |
pCRPMM 2d | powered Chinese restaurant process mixture model for 2d data |
SubCRP | several test on SubCRP mixture model (Bayesian variable selection for high-dimensional data in CRP) |
CSIGMM | demo for constrained sampling for CRPMM |
CRP draw | A basic demo for CRP prior draw |
- Adaptive Rejection Sampling (ARS) - Python implementation of ARS.
- Clustering accuracy - infopy: Python implementation of information theory computation.
- See requirements.txt
MIT
[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.