AMIDST Toolbox

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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.

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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AMIDST Toolbox

Präsentationen

Flink Forward 2016

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach (PGM2016)

d-VMP: Distributed Variational Message Passing (PGM2016)

Amidst demo (BNAIC 2015)

Parallelisation of the PC Algorithm (CAEPIA2015)

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs (ECAI2016)

Dynamic Bayesian modeling for risk prediction in credit operations (SCAI2015)

Analysis of massive data using R (CAEPIA2015)

Dokumente

A Java Toolbox for Analysis of MassIve Data STreams using Probabilistic Graphical Models (EDF2016)