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@rcabanasdepaz rcabanasdepaz released this 15 Sep 16:50
· 61 commits to master since this release
v0.6.3
9341bd0

This toolbox aims to offers a collection of scalable and parallel algorithms for inference and learning with probabilistic graphical models from local and distributed (streaming) data.

If you want to try the toolbox, visit https://github.com/amidst/example-project.

Changes:

  • Fixed some bugs
  • Added functionality for handling concept drift as detailed in:

Masegosa, A., Nielsen, T. D., Langseth, H., Ramos-Lopez, D., Salmerón, A., & Madsen, A. L.
(2017). Bayesian Models of Data Streams with Hierarchical Power Priors. Proceedings of
Thirty-fourth International Conference on Machine Learning (ICML’17). Sydney (Australia).

Release Date: 15/09/2017
Further Information: Project Web Page,JavaDoc

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