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AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning

Abstract · Apr 4, 2017 11:58 ·


Arxiv Abstract

  • Andrés R. Masegosa
  • Ana M. Martínez
  • Darío Ramos-López
  • Rafael Cabañas
  • Antonio Salmerón
  • Thomas D. Nielsen
  • Helge Langseth
  • Anders L. Madsen

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 under the Apache Software License version 2.0.

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