Amidst Toolbox Versions Save

This toolbox aims to offer a collection of scalable and parallel algorithms for inference and learning with probabilistic graphical models from local and distributed (streaming) data.

If you want to try the toolbox, visit https://github.com/amidst/example-project.

Changes:

• Fixed Xdoclint error in maven>3

Release Date: 04/09/2018 Further Information: Project Web Page,JavaDoc

This toolbox aims to offer a collection of scalable and parallel algorithms for inference and learning with probabilistic graphical models from local and distributed (streaming) data.

If you want to try the toolbox, visit https://github.com/amidst/example-project.

Changes:

• Fixed some bugs
• Changed the output of the inference algorithms

Release Date: 25/04/2018 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.

If you want to try the toolbox, visit https://github.com/amidst/example-project.

Changes:

• Fixed some bugs (#93)
• Added functionality to fix prior constraints to the parameters. A new tutorial on that coming soon.

Release Date: 18/01/2018 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.

If you want to try the toolbox, visit https://github.com/amidst/example-project.

Changes:

• Fixed some bugs
• Added functionality for handling concept drift as detailed in:

Masegosa, A., Nielsen, T. D., Langseth, H., Ramos-Lopez, D., Salmerón, A., & Madsen, A. L. (2017). Bayesian Models of Data Streams with Hierarchical Power Priors. Proceedings of Thirty-fourth International Conference on Machine Learning (ICML’17). Sydney (Australia).

Release Date: 15/09/2017 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 reported by @gunjanthesystem

Release Date: 07/03/2017 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:

• Unified loading streams names
• Fixed some bugs

Release Date: 03/01/2017 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 4.3, 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.

Changes:

• Added sparklink module implementing the integration with Apache Spark. More information here.
• Fluent pattern in latent-variable-models
• Predefined model implementing the concept drift detection
• Fixed some bugs

Release Date: 14/10/2016 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