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
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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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