AMIDST: A Java toolbox for scalable probabilistic machine learning
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Probabilistic graphical models,Scalable algorithms,Variational methods,Latent variables
论文评审过程：Received 8 January 2018, Revised 7 September 2018, Accepted 11 September 2018, Available online 29 September 2018, Version of Record 21 November 2018.
论文官网地址：https://doi.org/10.1016/j.knosys.2018.09.019
