Amidst Toolbox Versions Save

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.

Changes:

• Added sparklink module implementing the integration with Apache Spark. More information here.
• Fixed some bugs

Release Date: 14/09/2016 Further Information: Project Web Page,JavaDoc

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.

Changes:

• Added Maven module called "module-all" for being able to load all the toolbox modules at once.
• Fixed some bugs

Release Date: 19/08/2016 Further Information: Project Web Page, JavaDoc

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

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

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

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

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

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

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