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arXiv · 1704.01427

AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning

Andrés R. Masegosa·Ana M. Martínez·Darío Ramos-López·Rafael Cabañas·Antonio Salmerón·Thomas D. Nielsen·Helge Langseth·Anders L. Madsen
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Abstract

The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data. The toolbox supports a flexible modeling language based on probabilistic graphical models with latent variables and temporal dependencies. The specified models can be learnt from large data sets using parallel or distributed implementa- tions of Bayesian learning algorithms for either streaming or batch data. These algorithms are based on a flexible variational message passing scheme, which supports discrete and continu- ous variables from a wide range of probability distributions. AMIDST also leverages existing functionality and algorithms by interfacing to software tools such as Flink, Spark, MOA, Weka, R and HUGIN. AMIDST is an open source toolbox written in Java and available at http://www.amidsttoolbox.com under the Apache Software License version 2.0.

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BibTeXRIS

Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen. 2017-04-04. AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. https://doi.org/10.1016/j.knosys.2018.09.019

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