AMIDST: A Java toolbox for scalable probabilistic machine learning

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...

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Vydáno v:Knowledge-based systems Ročník 163; s. 595 - 597
Hlavní autoři: Masegosa, Andrés R., Martínez, Ana M., Ramos-López, Darío, Cabañas, Rafael, Salmerón, Antonio, Langseth, Helge, Nielsen, Thomas D., Madsen, Anders L.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.01.2019
Elsevier Science Ltd
Témata:
Algorithms
Artificial intelligence
Bayesian analysis
Continuity (mathematics)
Copyright
Graphical models
Inference
Java
Latent variables
Machine learning
Mathematical models
Message passing
Probabilistic graphical models
Probabilistic inference
Probability
Scalable algorithms
Statistical analysis
Variables
Variational methods
Latent variables
Probabilistic graphical models
Variational methods
Scalable algorithms
ISSN:0950-7051, 1872-7409
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Popis
Shrnutí: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. •AMIDST is an open source toolbox for scalable probabilistic machine learning.•The toolbox allows the definition of PGMs with latent variables.•AMIDST contains multiple scalable inference and learning algorithms.•The variational methods provided make the toolbox suitable for data streams.•The algorithms can be run in multi-core and distributed environments.
Bibliografie:ObjectType-Article-1
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.09.019