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

doi:10.1016/j.knosys.2018.09.019

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 journal › Journal article › Research › peer-review

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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.

Original language	English
Journal	Knowledge-Based Systems
Volume	163
Pages (from-to)	595-597
Number of pages	3
ISSN	0950-7051
DOIs	https://doi.org/10.1016/j.knosys.2018.09.019
Publication status	Published - 1 Jan 2019

Keywords

Probabilistic graphical models
Scalable algorithms
Variational methods
Latent variables

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10.1016/j.knosys.2018.09.019

AMIDST- A Java toolbox for scalable probabilistic machine learningAccepted author manuscript, 522 KBLicence: CC BY-NC-ND 4.0

https://arxiv.org/pdf/1704.01427.pdf

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title = "AMIDST: A Java toolbox for scalable probabilistic machine learning",

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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AU - Martinez, Ana M.

AU - Ramos-López, Darío

AU - Cabanas de Paz, Rafael

AU - Salmerón, Antonio

AU - Langseth, Helge

AU - Nielsen, Thomas Dyhre

AU - Madsen, Anders Læsø

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AB - 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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KW - Scalable algorithms

KW - Variational methods

KW - Latent variables

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DO - 10.1016/j.knosys.2018.09.019

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JO - Knowledge-Based Systems

JF - Knowledge-Based Systems

ER -

AMIDST: A Java toolbox for scalable probabilistic machine learning

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Keywords

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