AMIDST: A Java toolbox for scalable probabilistic machine learning

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Abstract

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.
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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
Volume163
Pages (from-to)595-597
Number of pages3
ISSN0950-7051
DOI
Publication statusPublished - 1 Jan 2019
Publication categoryResearch
Peer-reviewedYes

    Research areas

  • Probabilistic graphical models, Scalable algorithms, Variational methods, Latent variables
ID: 290548532