Release v0.5.0
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.
Added functionalities:
- Support to Flink for distributed learning of probabilistic models using novel scalable variational message passing based algorithms
- Support for multi-core or distributed learning of Latent Dirichlet Allocation Models
Release Date: 06/07/2016
Further Information: Project Web Page, JavaDoc