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.
Changes:
- Fixed some bugs
Release Date: 15/07/2016 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: 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:
- 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 multi-core parallel Bayesian learning using Java streams.
Release Date: 31/12/2015 Further Information: Deliverable 4.4, 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 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
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