The AMIDST Toolbox allows you to model your problem using a flexible probabilistic language based on graphical models. Then you fit your model with data using a Bayesian approach to handle modeling uncertainty. AMIDST provides tailored parallel (powered by Java 8 Streams) and distributed (powered by Flink or Spark) implementations of Bayesian parameter learning for batch and streaming data. This processing is based on flexible and scalable message passing algorithms. Data Streams: Update your models when new data is available.

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.