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Technology / Software / Internet
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www.amidsttoolbox.com
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AMIDST is an is an open source toolbox coded in Java for probabilistic machine learning. It provides tailored parallel and distributed implementation of Bayesian parameter learning (and probabilistic inference) for batch and streaming data. This processing is based on flexible and scalable message passing algorithms. This software was performed as part of the AMIDST project. AMIDST has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 619209.
Tags
bayesian networks
data streams
caepia2015
pgms
apache flink
machine learning
r
variational bayes
scai2015
dynamic bayesian modeling
streaming data
credit operations
feature subset selection
edf2016
software
toolbox
feature selection
ecai2016
pc algorithm
parallelisation
bayesian network
scalable learning
bayesian learning
variational inference
hybrid bayesian networks
pgm
scalable algorithms
map inference
java
flink
large-scale data
probabilistic graphical models
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(1)Personal Information
Branche
Technology / Software / Internet
Webseite
www.amidsttoolbox.com
Info
AMIDST is an is an open source toolbox coded in Java for probabilistic machine learning. It provides tailored parallel and distributed implementation of Bayesian parameter learning (and probabilistic inference) for batch and streaming data. This processing is based on flexible and scalable message passing algorithms. This software was performed as part of the AMIDST project. AMIDST has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 619209.
Tags
bayesian networks
data streams
caepia2015
pgms
apache flink
machine learning
r
variational bayes
scai2015
dynamic bayesian modeling
streaming data
credit operations
feature subset selection
edf2016
software
toolbox
feature selection
ecai2016
pc algorithm
parallelisation
bayesian network
scalable learning
bayesian learning
variational inference
hybrid bayesian networks
pgm
scalable algorithms
map inference
java
flink
large-scale data
probabilistic graphical models
Mehr anzeigen