  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.
