AMIDST: A Java toolbox for scalable probabilistic machine learning

Andres Masegosa, Ana M. Martinez, Darío Ramos-López, Rafael Cabanas de Paz, Antonio Salmerón, Helge Langseth, Thomas Dyhre Nielsen, Anders Læsø Madsen

Research output: Contribution to journalJournal articleResearchpeer-review

4 Citations (Scopus)


The AMIDST Toolbox is an open source Java software for scalable probabilistic machine learning with a special focus on (massive) streaming data. The toolbox supports a flexible modelling language based on probabilistic graphical models with latent variables. AMIDST provides parallel and distributed implementations of scalable algorithms for doing probabilistic inference and Bayesian parameter learning in the specified models. These algorithms are based on a flexible variational message passing scheme, which supports discrete and continuous variables from a wide range of probability distributions.
Original languageEnglish
JournalKnowledge-Based Systems
Pages (from-to)595-597
Number of pages3
Publication statusPublished - 1 Jan 2019


  • Probabilistic graphical models
  • Scalable algorithms
  • Variational methods
  • Latent variables

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  • Cite this

    Masegosa, A., Martinez, A. M., Ramos-López, D., Cabanas de Paz, R., Salmerón, A., Langseth, H., Nielsen, T. D., & Madsen, A. L. (2019). AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowledge-Based Systems, 163, 595-597.