AMIDST Toolbox
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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.
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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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Präsentationen (8)
Alle anzeigen
Flink Forward 2016
Vor 6 Jahren • 235 Aufrufe
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach (PGM2016)
Vor 6 Jahren • 264 Aufrufe
d-VMP: Distributed Variational Message Passing (PGM2016)
Vor 6 Jahren • 401 Aufrufe
Amidst demo (BNAIC 2015)
Vor 6 Jahren • 116 Aufrufe
Parallelisation of the PC Algorithm (CAEPIA2015)
Vor 6 Jahren • 180 Aufrufe
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs (ECAI2016)
Vor 6 Jahren • 355 Aufrufe
Dynamic Bayesian modeling for risk prediction in credit operations (SCAI2015)
Vor 6 Jahren • 778 Aufrufe
Analysis of massive data using R (CAEPIA2015)
Vor 6 Jahren • 542 Aufrufe
Dokumente (1)
A Java Toolbox for Analysis of MassIve Data STreams using Probabilistic Graphical Models (EDF2016)
Vor 6 Jahren • 322 Aufrufe
Präsentationen (8)
Alle anzeigen
Flink Forward 2016
Vor 6 Jahren • 235 Aufrufe
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach (PGM2016)
Vor 6 Jahren • 264 Aufrufe
d-VMP: Distributed Variational Message Passing (PGM2016)
Vor 6 Jahren • 401 Aufrufe
Amidst demo (BNAIC 2015)
Vor 6 Jahren • 116 Aufrufe
Parallelisation of the PC Algorithm (CAEPIA2015)
Vor 6 Jahren • 180 Aufrufe
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs (ECAI2016)
Vor 6 Jahren • 355 Aufrufe
Dynamic Bayesian modeling for risk prediction in credit operations (SCAI2015)
Vor 6 Jahren • 778 Aufrufe
Analysis of massive data using R (CAEPIA2015)
Vor 6 Jahren • 542 Aufrufe
Dokumente (1)
A Java Toolbox for Analysis of MassIve Data STreams using Probabilistic Graphical Models (EDF2016)
Vor 6 Jahren • 322 Aufrufe
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