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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.
OriginalsprogEngelsk
TidsskriftKnowledge-Based Systems
Vol/bind163
Sider (fra-til)595-597
Antal sider3
ISSN0950-7051
DOI
StatusUdgivet - 1 jan. 2019

Fingeraftryk

Learning systems
Message passing
Probability distributions
Java
Machine learning
Modeling languages
Latent variables
Software
Open source
Probability distribution
Inference
Language modeling
Graphical models

Emneord

    Citer dette

    Masegosa, A., Martinez, A. M., Ramos-López, D., Cabanas de Paz, R., Salmerón, A., Langseth, H., ... Madsen, A. L. (2019). AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowledge-Based Systems, 163, 595-597. https://doi.org/10.1016/j.knosys.2018.09.019
    Masegosa, Andres ; Martinez, Ana M. ; Ramos-López, Darío ; Cabanas de Paz, Rafael ; Salmerón, Antonio ; Langseth, Helge ; Nielsen, Thomas Dyhre ; Madsen, Anders Læsø. / AMIDST: A Java toolbox for scalable probabilistic machine learning. I: Knowledge-Based Systems. 2019 ; Bind 163. s. 595-597.
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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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    author = "Andres Masegosa and Martinez, {Ana M.} and Dar{\'i}o Ramos-L{\'o}pez and {Cabanas de Paz}, Rafael and Antonio Salmer{\'o}n and Helge Langseth and Nielsen, {Thomas Dyhre} and Madsen, {Anders L{\ae}s{\o}}",
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    Masegosa, A, Martinez, AM, Ramos-López, D, Cabanas de Paz, R, Salmerón, A, Langseth, H, Nielsen, TD & Madsen, AL 2019, 'AMIDST: A Java toolbox for scalable probabilistic machine learning' Knowledge-Based Systems, bind 163, s. 595-597. https://doi.org/10.1016/j.knosys.2018.09.019

    AMIDST: A Java toolbox for scalable probabilistic machine learning. / Masegosa, Andres; Martinez, Ana M.; Ramos-López, Darío; Cabanas de Paz, Rafael; Salmerón, Antonio; Langseth, Helge; Nielsen, Thomas Dyhre; Madsen, Anders Læsø.

    I: Knowledge-Based Systems, Bind 163, 01.01.2019, s. 595-597.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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    Masegosa A, Martinez AM, Ramos-López D, Cabanas de Paz R, Salmerón A, Langseth H et al. AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowledge-Based Systems. 2019 jan 1;163:595-597. https://doi.org/10.1016/j.knosys.2018.09.019