DPMMSubClusters.jl

Package provides an easy, fast and scalable way to perform inference in Dirichlet Process Mixture Models.

https://github.com/BGU-CS-VIL/DPMMSubClusters.jl

Developed from the code of:

Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia by Dinari et al.

Which is based on the algorithm from:

Parallel Sampling of DP Mixture Models using Sub-Clusters Splits by Chang and Fisher.

The package currently supports Gaussian and Multinomial priors, however adding your own is very easy, and more will come in future releases.

Examples:

2d Gaussian with plotting

Image Segmentation

Example of running from a params file, including saving and loading

If you use this package in your research, please cite the following:

@inproceedings{dinari2019distributed,
  title={Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia},
  author={Dinari, Or and Yu, Angel and Freifeld, Oren and Fisher III, John W},
  booktitle={2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)},
  pages={518--525},
  year={2019}
}

For any questions: dinari@post.bgu.ac.il Also available on Julia's Slack.

Contributions, feature requests, suggestion etc.. are welcomed.