amidst/toolbox

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.