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.
@article{arxiv.1704.01427,
title = {AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning},
author = {Andrés R. Masegosa and Ana M. Martínez and Darío Ramos-López and Rafael Cabañas and Antonio Salmerón and Thomas D. Nielsen and Helge Langseth and Anders L. Madsen},
journal= {arXiv preprint arXiv:1704.01427},
year = {2022}
}