AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning
This provides a practical software solution for researchers and practitioners dealing with large-scale streaming data, though it is incremental as it builds on existing tools and methods.
The authors tackled the challenge of scalable probabilistic machine learning for massive streaming data by developing AMIDST, a Java toolbox that supports flexible modeling with probabilistic graphical models and achieves efficient learning through parallel or distributed Bayesian algorithms.
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.