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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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