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Analysis of massive data using R (CAEPIA2015)

analysis-of-massive-data-using-r-caepia2015

AMIDST Toolbox

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

dynamic-bayesian-modeling-for-risk-prediction-in-credit-operations-scai2015

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block De...

parallel-filterbased-feature-selection-based-on-balanced-incomplete-block-designs-ecai2016

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

Parallelisation of the PC Algorithm (CAEPIA2015)

parallelisation-of-the-pc-algorithm-caepia2015

Amidst demo (BNAIC 2015)

amidst-demo-bnaic-2015

d-VMP: Distributed Variational Message Passing (PGM2016)

dvmp-distributed-variational-message-passing-pgm2016

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach (P...

scalable-map-inference-in-bayesian-networks-based-on-a-mapreduce-approach-pgm2016

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

Flink Forward 2016

flink-forward-2016

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.