From 50e3f46da753d8a7f01fc0ea59443d91d875e3e0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rafael=20Caba=C3=B1as=20de=20Paz?= Date: Wed, 6 Jul 2016 13:04:16 +0200 Subject: [PATCH] Update CHANGELOG.md --- CHANGELOG.md | 108 ++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 98 insertions(+), 10 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 9d5bc81fb..f4f981698 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,15 +1,103 @@ -0.4.2 / 2014-04-22 +v0.5.0 ================== - * Corrected errors in javadocs (issue #19) - * Renamed classifier package in standardmodels (issue #21) - * Removed references to non-publsihed modules (issue #23) +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) -0.4.1 / 2014-04-18 -================== - * Equal to 0.4.1-alpha but published having passed the tests and the artifacts are signed. +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. -0.4.1-alpha / 2014-04-17 -================== - * First version with deployed maven artifacts (unstable version) +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)