tag v0.5.2

Author: rcabanasdepaz

CHANGELOG.md

@@ -0,0 +1,129 @@
+v0.5.2
+==================
+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. 
+
+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](https://amidst.github.io/toolbox/),[JavaDoc](http://amidst.github.io/toolbox/javadoc/0.5.2/index.html)
+
+
+
+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. 
+
+Changes:
+- Fixed some bugs
+ 
+**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)
+
+
+
+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. 
+- Support for Latent Dirichlet Allocation Models
+ 
+**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)
+
+
+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](http://www.cs.waikato.ac.nz/ml/weka/)
+
+Minor changes: 
+
+- Module standardmodels has been renamed as latent-variable-models 
+ 
+**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)
+
+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.
+
+![Latent Variable Models](http://amidst.github.io/toolbox/docs/web/figs/amidstModels-crop.png)
+
+**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)
+
+
+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 4.4](https://amidst.github.io/toolbox/docs/deliverables/D4.3.pdf), [JavaDoc](http://amidst.github.io/toolbox/javadoc/0.4.1/index.html)
+
+
+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](http://moa.cs.waikato.ac.nz)
+
+**Release Date**: 30/11/2015
+**Further Information**: [Deliverable 3.3](https://amidst.github.io/toolbox/docs/deliverables/D3.3.pdf)
+
+
+
+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](http://www.hugin.com)
+
+
+**Release Date**: 31/06/2015
+**Further Information**: [Deliverable 3.2](https://amidst.github.io/toolbox/docs/deliverables/D3.2.pdf)
+
+
+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](https://amidst.github.io/toolbox/docs/deliverables/D2.3.pdf)
+
+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](https://amidst.github.io/toolbox/docs/deliverables/D4.1.pdf)