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

Release v0.5.0-alpha

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.
-Support for Latent Dirichlet Allocation Models

Release Date: 01/07/2016
Further Information: Project Web Page

Release v0.4.3

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:

• Bugs fixed
• Link to the Weka

Minor changes:

• Module standardmodels has been renamed as latent-variable-models

Release Date: 01/06/2016
Further Information: Project Web Page, JavaDoc

Release v0.4.2

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:

• A wide range of latent variable models coded in the toolbox as a proof-of-concept of the flexibility of our toolbox.

Release Date: 02/05/2016
Further Information: Project Web Page, JavaDoc

Release v0.4.1

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 multi-core parallel Bayesian learning using Java streams.

Release Date: 31/12/2015
Further Information: Deliverable 4.3, JavaDoc

Release v0.4

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

Release v0.3

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 Bayesian parameter learning in both static and dynamic Bayesian networks.
• Support for scalable Importance sampling for performing probabilistic queries.
• Link to Hugin

Release Date: 31/06/2015
Further Information: Deliverable 3.2

Release v0.2

This toolbox aims to offers a collection of scalable and parallel algorithms for inference and learning of both static and dynamic Bayesian networks from streaming data.

Added Functionalities:

• Support for representing dynamic Bayesian networks.
• Support for loading data sets with dynamic data instances.

Release Date: 31/03/2015
Further Information: Deliverable 2.3

Release v0.1

This is first release of the toolbox. This toolbox aims to offers a collection of scalable and parallel algorithms for inference and learning of both static and dynamic Bayesian networks from streaming data.

Functionalities:

• Support for representing static Bayesian networks.
• Support for loading streaming data sets.

Release Date: 31/12/2014
Further Information: Deliverable 4.1