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@andresmasegosa andresmasegosa released this 05 Dec 09:22
· 4530 commits to master since this release
v0.4
929677e

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 for approximate inference in dynamic Bayesian networks through the Factored Frontier algorithm.
  • Support for MAP and MPE inference in static Bayesian networks.
  • Link with MOA software

Release Date: 30/11/2015
Further Information: Deliverable 3.3

Assets 2