Dear Colleague, We are pleased to present you the AMIDST toolbox, which is an open source software coded in Java for analysis of large-scale data sets using probabilistic machine learning models. AMIDST runs algorithms in a multi-core and distributed fashion for learning and inference in a wide spectrum of latent variable models such as Gaussian mixtures, (probabilistic) principal component analysis, Hidden Markov Models, Kalman Filters, Latent Dirichlet Allocation, etc. This toolbox is able to perform Bayesian parameter learning on any user-defined probabilistic (graphical) model with billions of nodes using novel distributed message passing algorithms.
The toolbox has the possibility of learning from large data streams, i.e., update the model as new data is continuously being generated. For learning from streaming data, we provide an implementation of the streaming variational Bayes method and our own proposal, dVMP, that can also be used in a distributed setting, both of them based on the VMP algorithm. The algorithms implemented in AMIDST are scalable and can leverage both multi-core and distributed architectures, the latter through integration with Apache Spark and Apache Flink. The toolbox is freely available at http://www.amidsttoolbox.com <http://www.amidsttoolbox.com/>. If you have any question about the toolbox or if you want to collaborate in the project, please do not hesitate to contact us: Web: http://www.amidsttoolbox.com <http://www.amidsttoolbox.com/> Email: [email protected] <mailto:[email protected]> Twitter: @amidsttoolbox <https://twitter.com/AmidstToolbox> Best Regards, The AMIDST team This software was performed as part of the AMIDST project. AMIDST has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 619209. _______________________________________________ uai mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/uai
