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.

|-- 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

What do we include:

• Chinese restaurant process mixture model (CRPMM)

• Powered Chinese restaurant process (pCRP) mixture model

1. See requirements.txt

MIT

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).

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