/ThunderBayes.jl

A Julia Package for Bayesian Nonparametric Analysis for Machine Learning

Primary LanguageJuliaMIT LicenseMIT

thunder

CI GitHub GitHub release (latest by date)

Plan

Research

  • Bierkens, J., Fearnhead, P., & Roberts, G. (2019). The zig-zag process and super-efficient sampling for Bayesian analysis of Big Data. The Annals of Statistics, 47(3). https://doi.org/10.1214/18-aos1715
  • Sachs, M., Sen, D., Lu, J., & Dunson, D. (2022). Posterior computation with the Gibbs zig-zag sampler. Bayesian Analysis, -1(-1). https://doi.org/10.1214/22-ba1319
  • Casella, G., Mengersen, K. L., Robert, C. P., & Titterington, D. M. (2002). Perfect samplers for mixtures of distributions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 777–790. https://doi.org/10.1111/1467-9868.00360
  • Neal, R. M. (2003). Slice sampling. The Annals of Statistics, 31(3). https://doi.org/10.1214/aos/1056562461

Recently I research these beyesian inferences.....

Now Under Development & Test

  • Already standard bayesian nonparametric clustering has been implemented.
  • Now we implement bayesian nonparametric time-series clustering.
  • Please wait alpha release.

Thunder Bayes

Bayesian Nonparametric Package for Machine Learning

We aim to develop bayesian nonparametric library by Scala & Julia. Lots of relate scientific papers are pubilshed. We organize the papers and develop useful library. Firstly We will implement clustering methods and aim to release the first alpha version. The academic papers for this project are below:

Clusterings

  • Nieto-Barajas, L. E., & Contreras-Cristán, A. (2014). A bayesian nonparametric approach for time series clustering. Bayesian Analysis, 9(1). https://doi.org/10.1214/13-ba852
  • Beraha, M., Guglielmi, A., & Quintana, F. A. (2021). The semi-hierarchical Dirichlet process and its application to clustering homogeneous distributions. Bayesian Analysis, 16(4). https://doi.org/10.1214/21-ba1278
  • Heller, K. A., & Ghahramani, Z. (2005). Bayesian hierarchical clustering. Proceedings of the 22nd International Conference on Machine Learning - ICML '05. https://doi.org/10.1145/1102351.1102389
  • Bacallado, S., Favaro, S., Power, S., & Trippa, L. (2021). Perfect sampling of the posterior in the hierarchical pitman–yor process. Bayesian Analysis, -1(-1). https://doi.org/10.1214/21-ba1269
  • Page, G. L., & Quintana, F. A. (2015). Predictions based on the clustering of heterogeneous functions via shape and subject-specific covariates. Bayesian Analysis, 10(2). https://doi.org/10.1214/14-ba919
  • Müller Peter. (2015). Bayesian nonparametric data analysis. Springer.

Inference

  • Ni, Y., Müller, P., Diesendruck, M., Williamson, S., Zhu, Y., & Ji, Y. (2019). Scalable Bayesian nonparametric clustering and classification. Journal of Computational and Graphical Statistics, 29(1), 53–65. https://doi.org/10.1080/10618600.2019.1624366
  • Casella, G., Mengersen, K. L., Robert, C. P., & Titterington, D. M. (2002). Perfect samplers for mixtures of distributions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 777–790. https://doi.org/10.1111/1467-9868.00360
  • Neal, R. M. (2003). Slice sampling. The Annals of Statistics, 31(3). https://doi.org/10.1214/aos/1056562461

Enviroment

  • Visual Studio Code - 1.68.1
  • julia - 1.7.2
  • Scala - 2.13.8
  • R - 4.2.0

Contact

Please send suggestions at issues and report bugs to okadaalgorithm@gmail.com.