Implementation of Markov Chain Bayesian Clustering techniques, including DPM and MFM, with an abstract Mixture Model and Component Model API.
Project description
# Bayesian Markov Chain Clustering
Implementation of Markov Chain Bayesian Clustering techniques, including DPM (Dirichlet Process Mixture Models [1]) and MFM (Mixture of Finite Mixtures [2]) mixture models, with an abstract Mixture Model and Component Model API.
Hyperparameter updates for DPM are implemented using an Empirical Bayes update procedure [3].
Final configuration selection is implemented using Least Squares clustering [4].
## Usage WIP!
## API WIP!
## References
- [1] Radford M. Neal (2000), “Markov Chain Sampling Methods for Dirichlet
Process Mixture Models”. Journal of Computational and Graphical Statistics, Vol. 9, No. 2.
- [2] Jeffrey W. Miller, Matthew T. Harrison (2018),
“Mixture Models with a Prior on the Number of Components”. Journal of the American Statistical Association, Vol. 113, Issue 521.
- [3] Jon D. McAuliffe, David M. Blei, Michael I. Jordan (2006),
“Nonparametric empirical Bayes for the Dirichlet process mixture model”. Statistics and Computing, Vol. 16, Issue 1.
- [4] David B. Dahl (2006), “Model-Based Clustering for Expression Data via a
Dirichlet Process Mixture Model”. Bayesian Inference for Gene Expression and Proteomics.
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