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v0.5.2 | ||
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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. | ||
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Changes: | ||
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- Added Maven module called "module-all" for being able to load all the toolbox modules at once. | ||
- Fixed some bugs | ||
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**Release Date**: 19/08/2016 | ||
**Further Information**: [Project Web Page](https://amidst.github.io/toolbox/),[JavaDoc](http://amidst.github.io/toolbox/javadoc/0.5.2/index.html) | ||
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v0.5.1 | ||
================== | ||
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. | ||
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Changes: | ||
- Fixed some bugs | ||
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**Release Date**: 15/07/2016 | ||
**Further Information**: [Project Web Page](https://amidst.github.io/toolbox/),[JavaDoc](http://amidst.github.io/toolbox/javadoc/0.5.1/index.html) | ||
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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. | ||
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Added functionalities: | ||
- Support to Flink for distributed learning of probabilistic models. | ||
- Support for Latent Dirichlet Allocation Models | ||
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**Release Date**: 06/07/2016 | ||
**Further Information**: [Project Web Page](https://amidst.github.io/toolbox/),[JavaDoc](http://amidst.github.io/toolbox/javadoc/0.5.0/index.html) | ||
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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. | ||
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Added functionalities: | ||
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- Bugs fixed | ||
- Link to the [Weka](http://www.cs.waikato.ac.nz/ml/weka/) | ||
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Minor changes: | ||
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- Module standardmodels has been renamed as latent-variable-models | ||
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**Release Date**: 01/06/2016 | ||
**Further Information**: [Project Web Page](https://amidst.github.io/toolbox/), [JavaDoc](http://amidst.github.io/toolbox/javadoc/0.4.3/index.html) | ||
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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. | ||
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Added functionalities: | ||
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- A wide range of latent variable models coded in the toolbox as a proof-of-concept of the flexibility of our toolbox. | ||
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![Latent Variable Models](http://amidst.github.io/toolbox/docs/web/figs/amidstModels-crop.png) | ||
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**Release Date**: 02/05/2016 | ||
**Further Information**: [Project Web Page](https://amidst.github.io/toolbox/), [JavaDoc](http://amidst.github.io/toolbox/javadoc/0.4.2/index.html) | ||
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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. | ||
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Added Functionalities: | ||
- Support for multi-core parallel Bayesian learning using Java streams. | ||
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**Release Date**: 31/12/2015 | ||
**Further Information**: [Deliverable 4.4](https://amidst.github.io/toolbox/docs/deliverables/D4.3.pdf), [JavaDoc](http://amidst.github.io/toolbox/javadoc/0.4.1/index.html) | ||
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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. | ||
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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](http://moa.cs.waikato.ac.nz) | ||
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**Release Date**: 30/11/2015 | ||
**Further Information**: [Deliverable 3.3](https://amidst.github.io/toolbox/docs/deliverables/D3.3.pdf) | ||
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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. | ||
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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](http://www.hugin.com) | ||
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**Release Date**: 31/06/2015 | ||
**Further Information**: [Deliverable 3.2](https://amidst.github.io/toolbox/docs/deliverables/D3.2.pdf) | ||
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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. | ||
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Added Functionalities: | ||
- Support for representing dynamic Bayesian networks. | ||
- Support for loading data sets with dynamic data instances. | ||
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**Release Date**: 31/03/2015 | ||
**Further Information**: [Deliverable 2.3](https://amidst.github.io/toolbox/docs/deliverables/D2.3.pdf) | ||
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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. | ||
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Functionalities: | ||
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- Support for representing static Bayesian networks. | ||
- Support for loading streaming data sets. | ||
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**Release Date**: 31/12/2014 | ||
**Further Information**: [Deliverable 4.1](https://amidst.github.io/toolbox/docs/deliverables/D4.1.pdf) |